######################################### House Rent Problem #################################################################

Problem Statement¶

Ask a home buyer to describe their dream house, and they probably won't begin with the height of the basement ceiling or the proximity to an east-west railroad. But this playground competition's dataset proves that much more influences price negotiations than the number of bedrooms or a white-picket fence.

With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this competition challenges you to predict the final price of each home.

Goal

It is your job to predict the sales price for each house. For each Id in the test set, you must predict the value of the SalePrice variable.

Metric

Submissions are evaluated on Root-Mean-Squared-Error (RMSE) between the logarithm of the predicted value and the logarithm of the observed sales price. (Taking logs means that errors in predicting expensive houses and cheap houses will affect the result equally.)

Reference Link : https://www.kaggle.com/c/house-prices-advanced-regression-techniques/overview/description

File descriptions

train.csv - the training set test.csv - the test set data_description.txt - full description of each column, originally prepared by Dean De Cock but lightly edited to match the column names used here sample_submission.csv - a benchmark submission from a linear regression on year and month of sale, lot square footage, and number of bedrooms

Data fields

Here's a brief version of what you'll find in the data description file.

SalePrice - the property's sale price in dollars. This is the target variable that you're trying to predict.

MSSubClass: The building class

MSZoning: The general zoning classification

LotFrontage: Linear feet of street connected to property

LotArea: Lot size in square feet

Street: Type of road access

Alley: Type of alley access

LotShape: General shape of property

LandContour: Flatness of the property

Utilities: Type of utilities available

LotConfig: Lot configuration

LandSlope: Slope of property

Neighborhood: Physical locations within Ames city limits

Condition1: Proximity to main road or railroad

Condition2: Proximity to main road or railroad (if a second is present)

BldgType: Type of dwelling

HouseStyle: Style of dwelling

OverallQual: Overall material and finish quality

OverallCond: Overall condition rating

YearBuilt: Original construction date

YearRemodAdd: Remodel date

RoofStyle: Type of roof

RoofMatl: Roof material

Exterior1st: Exterior covering on house

Exterior2nd: Exterior covering on house (if more than one material)

MasVnrType: Masonry veneer type

MasVnrArea: Masonry veneer area in square feet

ExterQual: Exterior material quality

ExterCond: Present condition of the material on the exterior

Foundation: Type of foundation

BsmtQual: Height of the basement

BsmtCond: General condition of the basement

BsmtExposure: Walkout or garden level basement walls

BsmtFinType1: Quality of basement finished area

BsmtFinSF1: Type 1 finished square feet

BsmtFinType2: Quality of second finished area (if present)

BsmtFinSF2: Type 2 finished square feet

BsmtUnfSF: Unfinished square feet of basement area

TotalBsmtSF: Total square feet of basement area

Heating: Type of heating

HeatingQC: Heating quality and condition

CentralAir: Central air conditioning

Electrical: Electrical system

1stFlrSF: First Floor square feet

2ndFlrSF: Second floor square feet

LowQualFinSF: Low quality finished square feet (all floors)

GrLivArea: Above grade (ground) living area square feet

BsmtFullBath: Basement full bathrooms

BsmtHalfBath: Basement half bathrooms

FullBath: Full bathrooms above grade

HalfBath: Half baths above grade

Bedroom: Number of bedrooms above basement level

Kitchen: Number of kitchens

KitchenQual: Kitchen quality

TotRmsAbvGrd: Total rooms above grade (does not include bathrooms)

Functional: Home functionality rating

Fireplaces: Number of fireplaces

FireplaceQu: Fireplace quality

GarageType: Garage location

GarageYrBlt: Year garage was built

GarageFinish: Interior finish of the garage

GarageCars: Size of garage in car capacity

GarageArea: Size of garage in square feet

GarageQual: Garage quality

GarageCond: Garage condition

PavedDrive: Paved driveway

WoodDeckSF: Wood deck area in square feet

OpenPorchSF: Open porch area in square feet

EnclosedPorch: Enclosed porch area in square feet

3SsnPorch: Three season porch area in square feet

ScreenPorch: Screen porch area in square feet

PoolArea: Pool area in square feet

PoolQC: Pool quality

Fence: Fence quality

MiscFeature: Miscellaneous feature not covered in other categories

MiscVal: $Value of miscellaneous feature

MoSold: Month Sold

YrSold: Year Sold

SaleType: Type of sale

SaleCondition: Condition of sale

## Import necessary libraries.

import numpy as np ## Numpy Library ( will use to convert data frame to array or creating array etc...).
import pandas as pd ## Pandas Library (will use to load data,create data frame...etc).
import os ## For connecting to machine to get path for reading/writing files.
from sklearn.model_selection import train_test_split ## For splitting data into train and validation.
import matplotlib.pyplot as plt ## For visualization.
import seaborn as sns ## For visualization.

## Get current working directory.
os.getcwd()

'D:\\Python\\Pratice'

## Set working directory
os.chdir("D:\\DataScience\\Pratice\\House_Rent_Price")

## Read the train data set.
data = pd.read_csv("train.csv",header='infer',sep=',')

## Read the test data set.
test_data = pd.read_csv("test.csv",header='infer',sep=',')

## Set how many rows and columns you want to display in jupyter notebook.
pd.options.display.max_columns = 200 
pd.get_option('display.max_rows') 
pd.set_option('display.max_rows',None)

## Check dimesnions of train data.
data.shape

(1460, 81)

## Check dimensions of test data.
test_data.shape

(1459, 80)

## Get first 5 records of train data.
data.head()

## Get first 5 records of train data.
test_data.head()

## Check summary statistics of train data.
data.describe(include='all')

## Check summary statistics of test data.
test_data.describe(include='all')

## Get columns data types of train data.
data.dtypes

Id                 int64
MSSubClass         int64
MSZoning          object
LotFrontage      float64
LotArea            int64
Street            object
Alley             object
LotShape          object
LandContour       object
Utilities         object
LotConfig         object
LandSlope         object
Neighborhood      object
Condition1        object
Condition2        object
BldgType          object
HouseStyle        object
OverallQual        int64
OverallCond        int64
YearBuilt          int64
YearRemodAdd       int64
RoofStyle         object
RoofMatl          object
Exterior1st       object
Exterior2nd       object
MasVnrType        object
MasVnrArea       float64
ExterQual         object
ExterCond         object
Foundation        object
BsmtQual          object
BsmtCond          object
BsmtExposure      object
BsmtFinType1      object
BsmtFinSF1         int64
BsmtFinType2      object
BsmtFinSF2         int64
BsmtUnfSF          int64
TotalBsmtSF        int64
Heating           object
HeatingQC         object
CentralAir        object
Electrical        object
1stFlrSF           int64
2ndFlrSF           int64
LowQualFinSF       int64
GrLivArea          int64
BsmtFullBath       int64
BsmtHalfBath       int64
FullBath           int64
HalfBath           int64
BedroomAbvGr       int64
KitchenAbvGr       int64
KitchenQual       object
TotRmsAbvGrd       int64
Functional        object
Fireplaces         int64
FireplaceQu       object
GarageType        object
GarageYrBlt      float64
GarageFinish      object
GarageCars         int64
GarageArea         int64
GarageQual        object
GarageCond        object
PavedDrive        object
WoodDeckSF         int64
OpenPorchSF        int64
EnclosedPorch      int64
3SsnPorch          int64
ScreenPorch        int64
PoolArea           int64
PoolQC            object
Fence             object
MiscFeature       object
MiscVal            int64
MoSold             int64
YrSold             int64
SaleType          object
SaleCondition     object
SalePrice          int64
dtype: object

## Get columns data types of test data.
test_data.dtypes

Id                 int64
MSSubClass         int64
MSZoning          object
LotFrontage      float64
LotArea            int64
Street            object
Alley             object
LotShape          object
LandContour       object
Utilities         object
LotConfig         object
LandSlope         object
Neighborhood      object
Condition1        object
Condition2        object
BldgType          object
HouseStyle        object
OverallQual        int64
OverallCond        int64
YearBuilt          int64
YearRemodAdd       int64
RoofStyle         object
RoofMatl          object
Exterior1st       object
Exterior2nd       object
MasVnrType        object
MasVnrArea       float64
ExterQual         object
ExterCond         object
Foundation        object
BsmtQual          object
BsmtCond          object
BsmtExposure      object
BsmtFinType1      object
BsmtFinSF1       float64
BsmtFinType2      object
BsmtFinSF2       float64
BsmtUnfSF        float64
TotalBsmtSF      float64
Heating           object
HeatingQC         object
CentralAir        object
Electrical        object
1stFlrSF           int64
2ndFlrSF           int64
LowQualFinSF       int64
GrLivArea          int64
BsmtFullBath     float64
BsmtHalfBath     float64
FullBath           int64
HalfBath           int64
BedroomAbvGr       int64
KitchenAbvGr       int64
KitchenQual       object
TotRmsAbvGrd       int64
Functional        object
Fireplaces         int64
FireplaceQu       object
GarageType        object
GarageYrBlt      float64
GarageFinish      object
GarageCars       float64
GarageArea       float64
GarageQual        object
GarageCond        object
PavedDrive        object
WoodDeckSF         int64
OpenPorchSF        int64
EnclosedPorch      int64
3SsnPorch          int64
ScreenPorch        int64
PoolArea           int64
PoolQC            object
Fence             object
MiscFeature       object
MiscVal            int64
MoSold             int64
YrSold             int64
SaleType          object
SaleCondition     object
dtype: object

## EDA

## Plot scatter matrix for train data.
pd.plotting.scatter_matrix(data, figsize=(75, 75), diagonal='kde')
plt.show()

## Plot scatter matrix for test data.
pd.plotting.scatter_matrix(test_data, figsize=(75, 75), diagonal='kde')
plt.show()

## Plot correlation matrix for train data.
plt.figure(figsize=(75,75))
sns.heatmap(data.corr(),cmap='coolwarm',annot = True)
plt.show()

## Plot correlation matrix for test data.
plt.figure(figsize=(75,75))
sns.heatmap(test_data.corr(),cmap='coolwarm',annot = True)
plt.show()

## Plot Probability plot for target varible.
from scipy import stats
#Get also the QQ-plot
fig = plt.figure()
res = stats.probplot(data['SalePrice'], plot=plt)
plt.show()

## Display distribution plot for target,GarageCars columns.
plt.figure(figsize=(16,8))
sns.boxplot(x='GarageCars',y='SalePrice',data=data)
plt.show()

## Display scatter plot for GarageArea,target columns.
sns.lmplot(x='GarageArea',y='SalePrice',data=data)

C:\Users\nagar\Anaconda3\lib\site-packages\scipy\stats\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval

<seaborn.axisgrid.FacetGrid at 0x209072cecf8>

## Saleprice correlation matrix
k = 10 ## Number of variables for heatmap
plt.figure(figsize=(16,8))
corrmat = data.corr()
## Picking the top 15 correlated features
cols = corrmat.nlargest(k, 'SalePrice')['SalePrice'].index
cm = np.corrcoef(data[cols].values.T)
sns.set(font_scale=1.25)
hm = sns.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 10}, yticklabels=cols.values, xticklabels=cols.values)
plt.show()

## Plot Histogram for target column.
sns.distplot(data['SalePrice']);

C:\Users\nagar\Anaconda3\lib\site-packages\scipy\stats\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval
C:\Users\nagar\Anaconda3\lib\site-packages\seaborn\distributions.py:218: MatplotlibDeprecationWarning: 
The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.
  color=hist_color, **hist_kws)

## Check skewness and kurtosis for target varible.
print("Skewness: %f" % data['SalePrice'].skew())
print("Kurtosis: %f" % data['SalePrice'].kurt())

Skewness: 1.882876
Kurtosis: 6.536282

## Scatter plot for some columns of train data.
sns.set()
cols = ['SalePrice', 'OverallQual', 'GrLivArea', 'GarageCars', 'TotalBsmtSF', 'YearBuilt']
sns.pairplot(data[cols], size = 2.5)
plt.show();

## Histogram and normal probability plot.
from scipy.stats import norm
sns.distplot(data['SalePrice'], fit=norm);
fig = plt.figure()
res = stats.probplot(data['SalePrice'], plot=plt)

C:\Users\nagar\Anaconda3\lib\site-packages\scipy\stats\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval
C:\Users\nagar\Anaconda3\lib\site-packages\seaborn\distributions.py:218: MatplotlibDeprecationWarning: 
The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.
  color=hist_color, **hist_kws)

## Display correlation plot in desecnding order.
corr = data.select_dtypes(include=['int64','float64']).corr()
plt.figure(figsize=(16,6))
corr['SalePrice'].sort_values(ascending=False)[1:].plot(kind='bar')
plt.tight_layout()

## Visualize missing data.
missing_value = data.isnull().sum().sort_values(ascending=False) / len(data) * 100
missing_value = missing_value[missing_value != 0]
missing_value = pd.DataFrame({'Missing value' :missing_value,'Type':missing_value.index.map(lambda x:data[x].dtype)})
missing_value.plot(kind='bar',figsize=(16,4))
plt.show()

## Display distribution plots.
quantitative = data.select_dtypes('int64')
f = pd.melt(data, value_vars=quantitative)
g = sns.FacetGrid(f, col="variable",  col_wrap=2, sharex=False, sharey=False)
g = g.map(sns.distplot, "value")

C:\Users\nagar\Anaconda3\lib\site-packages\scipy\stats\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval
C:\Users\nagar\Anaconda3\lib\site-packages\seaborn\distributions.py:218: MatplotlibDeprecationWarning: 
The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.
  color=hist_color, **hist_kws)
C:\Users\nagar\Anaconda3\lib\site-packages\seaborn\distributions.py:218: MatplotlibDeprecationWarning: 
The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.
  color=hist_color, **hist_kws)
C:\Users\nagar\Anaconda3\lib\site-packages\seaborn\distributions.py:218: MatplotlibDeprecationWarning: 
The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.
  color=hist_color, **hist_kws)
C:\Users\nagar\Anaconda3\lib\site-packages\seaborn\distributions.py:218: MatplotlibDeprecationWarning: 
The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.
  color=hist_color, **hist_kws)
C:\Users\nagar\Anaconda3\lib\site-packages\seaborn\distributions.py:218: MatplotlibDeprecationWarning: 
The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.
  color=hist_color, **hist_kws)
C:\Users\nagar\Anaconda3\lib\site-packages\seaborn\distributions.py:218: MatplotlibDeprecationWarning: 
The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.
  color=hist_color, **hist_kws)
C:\Users\nagar\Anaconda3\lib\site-packages\seaborn\distributions.py:218: MatplotlibDeprecationWarning: 
The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.
  color=hist_color, **hist_kws)
C:\Users\nagar\Anaconda3\lib\site-packages\seaborn\distributions.py:218: MatplotlibDeprecationWarning: 
The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.
  color=hist_color, **hist_kws)
C:\Users\nagar\Anaconda3\lib\site-packages\seaborn\distributions.py:218: MatplotlibDeprecationWarning: 
The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.
  color=hist_color, **hist_kws)
C:\Users\nagar\Anaconda3\lib\site-packages\seaborn\distributions.py:218: MatplotlibDeprecationWarning: 
The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.
  color=hist_color, **hist_kws)
C:\Users\nagar\Anaconda3\lib\site-packages\seaborn\distributions.py:218: MatplotlibDeprecationWarning: 
The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.
  color=hist_color, **hist_kws)
C:\Users\nagar\Anaconda3\lib\site-packages\seaborn\distributions.py:218: MatplotlibDeprecationWarning: 
The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.
  color=hist_color, **hist_kws)
C:\Users\nagar\Anaconda3\lib\site-packages\seaborn\distributions.py:218: MatplotlibDeprecationWarning: 
The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.
  color=hist_color, **hist_kws)
C:\Users\nagar\Anaconda3\lib\site-packages\seaborn\distributions.py:218: MatplotlibDeprecationWarning: 
The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.
  color=hist_color, **hist_kws)
C:\Users\nagar\Anaconda3\lib\site-packages\seaborn\distributions.py:218: MatplotlibDeprecationWarning: 
The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.
  color=hist_color, **hist_kws)
C:\Users\nagar\Anaconda3\lib\site-packages\seaborn\distributions.py:218: MatplotlibDeprecationWarning: 
The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.
  color=hist_color, **hist_kws)
C:\Users\nagar\Anaconda3\lib\site-packages\seaborn\distributions.py:218: MatplotlibDeprecationWarning: 
The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.
  color=hist_color, **hist_kws)
C:\Users\nagar\Anaconda3\lib\site-packages\seaborn\distributions.py:218: MatplotlibDeprecationWarning: 
The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.
  color=hist_color, **hist_kws)
C:\Users\nagar\Anaconda3\lib\site-packages\seaborn\distributions.py:218: MatplotlibDeprecationWarning: 
The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.
  color=hist_color, **hist_kws)
C:\Users\nagar\Anaconda3\lib\site-packages\seaborn\distributions.py:218: MatplotlibDeprecationWarning: 
The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.
  color=hist_color, **hist_kws)
C:\Users\nagar\Anaconda3\lib\site-packages\seaborn\distributions.py:218: MatplotlibDeprecationWarning: 
The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.
  color=hist_color, **hist_kws)
C:\Users\nagar\Anaconda3\lib\site-packages\seaborn\distributions.py:218: MatplotlibDeprecationWarning: 
The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.
  color=hist_color, **hist_kws)
C:\Users\nagar\Anaconda3\lib\site-packages\seaborn\distributions.py:218: MatplotlibDeprecationWarning: 
The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.
  color=hist_color, **hist_kws)
C:\Users\nagar\Anaconda3\lib\site-packages\seaborn\distributions.py:218: MatplotlibDeprecationWarning: 
The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.
  color=hist_color, **hist_kws)
C:\Users\nagar\Anaconda3\lib\site-packages\seaborn\distributions.py:218: MatplotlibDeprecationWarning: 
The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.
  color=hist_color, **hist_kws)
C:\Users\nagar\Anaconda3\lib\site-packages\seaborn\distributions.py:218: MatplotlibDeprecationWarning: 
The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.
  color=hist_color, **hist_kws)
C:\Users\nagar\Anaconda3\lib\site-packages\seaborn\distributions.py:218: MatplotlibDeprecationWarning: 
The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.
  color=hist_color, **hist_kws)
C:\Users\nagar\Anaconda3\lib\site-packages\seaborn\distributions.py:218: MatplotlibDeprecationWarning: 
The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.
  color=hist_color, **hist_kws)
C:\Users\nagar\Anaconda3\lib\site-packages\seaborn\distributions.py:218: MatplotlibDeprecationWarning: 
The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.
  color=hist_color, **hist_kws)
C:\Users\nagar\Anaconda3\lib\site-packages\seaborn\distributions.py:218: MatplotlibDeprecationWarning: 
The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.
  color=hist_color, **hist_kws)
C:\Users\nagar\Anaconda3\lib\site-packages\seaborn\distributions.py:218: MatplotlibDeprecationWarning: 
The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.
  color=hist_color, **hist_kws)
C:\Users\nagar\Anaconda3\lib\site-packages\seaborn\distributions.py:218: MatplotlibDeprecationWarning: 
The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.
  color=hist_color, **hist_kws)
C:\Users\nagar\Anaconda3\lib\site-packages\seaborn\distributions.py:218: MatplotlibDeprecationWarning: 
The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.
  color=hist_color, **hist_kws)
C:\Users\nagar\Anaconda3\lib\site-packages\seaborn\distributions.py:218: MatplotlibDeprecationWarning: 
The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.
  color=hist_color, **hist_kws)
C:\Users\nagar\Anaconda3\lib\site-packages\seaborn\distributions.py:218: MatplotlibDeprecationWarning: 
The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.
  color=hist_color, **hist_kws)

## Display distribution plots.
qualitative = data.select_dtypes(['object','category'])
temp = data
for c in qualitative:
    temp[c] = temp[c].astype('category')
    if temp[c].isnull().any():
        temp[c] = temp[c].cat.add_categories(['MISSING'])
        temp[c] = temp[c].fillna('MISSING')

def boxplot(x, y, **kwargs):
    sns.boxplot(x=x, y=y)
    x=plt.xticks(rotation=90)
f = pd.melt(temp, id_vars=['SalePrice'], value_vars=qualitative)
g = sns.FacetGrid(f, col="variable",  col_wrap=2, sharex=False, sharey=False, size=5)
g = g.map(boxplot, "value", "SalePrice")

## Plot histograms for train data.
(data.select_dtypes(include = ['float64', 'int64'])).hist(figsize=(16, 20), bins=50, xlabelsize=8, ylabelsize=8);

## Pair Plot.
temp_1 =data.select_dtypes(include = ['float64', 'int64'])
for i in range(0, len(temp_1.columns), 5):
    sns.pairplot(data=temp_1,
                x_vars=temp_1.columns[i:i+5],
                y_vars=['SalePrice'])

## Magic command.
%matplotlib inline

## Get missing values for train data.
data.isna().sum()

Id                  0
MSSubClass          0
MSZoning            0
LotFrontage       259
LotArea             0
Street              0
Alley            1369
LotShape            0
LandContour         0
Utilities           0
LotConfig           0
LandSlope           0
Neighborhood        0
Condition1          0
Condition2          0
BldgType            0
HouseStyle          0
OverallQual         0
OverallCond         0
YearBuilt           0
YearRemodAdd        0
RoofStyle           0
RoofMatl            0
Exterior1st         0
Exterior2nd         0
MasVnrType          8
MasVnrArea          8
ExterQual           0
ExterCond           0
Foundation          0
BsmtQual           37
BsmtCond           37
BsmtExposure       38
BsmtFinType1       37
BsmtFinSF1          0
BsmtFinType2       38
BsmtFinSF2          0
BsmtUnfSF           0
TotalBsmtSF         0
Heating             0
HeatingQC           0
CentralAir          0
Electrical          1
1stFlrSF            0
2ndFlrSF            0
LowQualFinSF        0
GrLivArea           0
BsmtFullBath        0
BsmtHalfBath        0
FullBath            0
HalfBath            0
BedroomAbvGr        0
KitchenAbvGr        0
KitchenQual         0
TotRmsAbvGrd        0
Functional          0
Fireplaces          0
FireplaceQu       690
GarageType         81
GarageYrBlt        81
GarageFinish       81
GarageCars          0
GarageArea          0
GarageQual         81
GarageCond         81
PavedDrive          0
WoodDeckSF          0
OpenPorchSF         0
EnclosedPorch       0
3SsnPorch           0
ScreenPorch         0
PoolArea            0
PoolQC           1453
Fence            1179
MiscFeature      1406
MiscVal             0
MoSold              0
YrSold              0
SaleType            0
SaleCondition       0
SalePrice           0
dtype: int64

## Get missing values for test data.
test_data.isna().sum()

Id                  0
MSSubClass          0
MSZoning            4
LotFrontage       227
LotArea             0
Street              0
Alley            1352
LotShape            0
LandContour         0
Utilities           2
LotConfig           0
LandSlope           0
Neighborhood        0
Condition1          0
Condition2          0
BldgType            0
HouseStyle          0
OverallQual         0
OverallCond         0
YearBuilt           0
YearRemodAdd        0
RoofStyle           0
RoofMatl            0
Exterior1st         1
Exterior2nd         1
MasVnrType         16
MasVnrArea         15
ExterQual           0
ExterCond           0
Foundation          0
BsmtQual           44
BsmtCond           45
BsmtExposure       44
BsmtFinType1       42
BsmtFinSF1          1
BsmtFinType2       42
BsmtFinSF2          1
BsmtUnfSF           1
TotalBsmtSF         1
Heating             0
HeatingQC           0
CentralAir          0
Electrical          0
1stFlrSF            0
2ndFlrSF            0
LowQualFinSF        0
GrLivArea           0
BsmtFullBath        2
BsmtHalfBath        2
FullBath            0
HalfBath            0
BedroomAbvGr        0
KitchenAbvGr        0
KitchenQual         1
TotRmsAbvGrd        0
Functional          2
Fireplaces          0
FireplaceQu       730
GarageType         76
GarageYrBlt        78
GarageFinish       78
GarageCars          1
GarageArea          1
GarageQual         78
GarageCond         78
PavedDrive          0
WoodDeckSF          0
OpenPorchSF         0
EnclosedPorch       0
3SsnPorch           0
ScreenPorch         0
PoolArea            0
PoolQC           1456
Fence            1169
MiscFeature      1408
MiscVal             0
MoSold              0
YrSold              0
SaleType            1
SaleCondition       0
dtype: int64

### Find missing values % for train data.
missing_value = (data.isna().sum()/len(data)).round(4)*100
missing_value.sort_values(ascending=False)
#missing_value.count

PoolQC           99.52
MiscFeature      96.30
Alley            93.77
Fence            80.75
FireplaceQu      47.26
LotFrontage      17.74
GarageCond        5.55
GarageType        5.55
GarageYrBlt       5.55
GarageFinish      5.55
GarageQual        5.55
BsmtExposure      2.60
BsmtFinType2      2.60
BsmtFinType1      2.53
BsmtCond          2.53
BsmtQual          2.53
MasVnrArea        0.55
MasVnrType        0.55
Electrical        0.07
Utilities         0.00
YearRemodAdd      0.00
MSSubClass        0.00
Foundation        0.00
ExterCond         0.00
ExterQual         0.00
Exterior2nd       0.00
Exterior1st       0.00
RoofMatl          0.00
RoofStyle         0.00
YearBuilt         0.00
LotConfig         0.00
OverallCond       0.00
OverallQual       0.00
HouseStyle        0.00
BldgType          0.00
Condition2        0.00
BsmtFinSF1        0.00
MSZoning          0.00
LotArea           0.00
Street            0.00
Condition1        0.00
Neighborhood      0.00
LotShape          0.00
LandContour       0.00
LandSlope         0.00
SalePrice         0.00
HeatingQC         0.00
BsmtFinSF2        0.00
EnclosedPorch     0.00
Fireplaces        0.00
GarageCars        0.00
GarageArea        0.00
PavedDrive        0.00
WoodDeckSF        0.00
OpenPorchSF       0.00
3SsnPorch         0.00
BsmtUnfSF         0.00
ScreenPorch       0.00
PoolArea          0.00
MiscVal           0.00
MoSold            0.00
YrSold            0.00
SaleType          0.00
Functional        0.00
TotRmsAbvGrd      0.00
KitchenQual       0.00
KitchenAbvGr      0.00
BedroomAbvGr      0.00
HalfBath          0.00
FullBath          0.00
BsmtHalfBath      0.00
BsmtFullBath      0.00
GrLivArea         0.00
LowQualFinSF      0.00
2ndFlrSF          0.00
1stFlrSF          0.00
CentralAir        0.00
SaleCondition     0.00
Heating           0.00
TotalBsmtSF       0.00
Id                0.00
dtype: float64

### Find missing values % for test data.
missing_value_test = (test_data.isna().sum()/len(test_data)).round(4)*100
missing_value_test.sort_values(ascending=False)

PoolQC           99.79
MiscFeature      96.50
Alley            92.67
Fence            80.12
FireplaceQu      50.03
LotFrontage      15.56
GarageCond        5.35
GarageQual        5.35
GarageYrBlt       5.35
GarageFinish      5.35
GarageType        5.21
BsmtCond          3.08
BsmtQual          3.02
BsmtExposure      3.02
BsmtFinType1      2.88
BsmtFinType2      2.88
MasVnrType        1.10
MasVnrArea        1.03
MSZoning          0.27
BsmtHalfBath      0.14
Utilities         0.14
Functional        0.14
BsmtFullBath      0.14
BsmtFinSF2        0.07
BsmtFinSF1        0.07
Exterior2nd       0.07
BsmtUnfSF         0.07
TotalBsmtSF       0.07
SaleType          0.07
Exterior1st       0.07
KitchenQual       0.07
GarageArea        0.07
GarageCars        0.07
HouseStyle        0.00
LandSlope         0.00
MSSubClass        0.00
LotArea           0.00
Street            0.00
LotShape          0.00
LandContour       0.00
LotConfig         0.00
Neighborhood      0.00
BldgType          0.00
Condition1        0.00
Condition2        0.00
RoofMatl          0.00
RoofStyle         0.00
YearRemodAdd      0.00
YearBuilt         0.00
OverallCond       0.00
OverallQual       0.00
SaleCondition     0.00
Heating           0.00
ExterQual         0.00
TotRmsAbvGrd      0.00
YrSold            0.00
MoSold            0.00
MiscVal           0.00
PoolArea          0.00
ScreenPorch       0.00
3SsnPorch         0.00
EnclosedPorch     0.00
OpenPorchSF       0.00
WoodDeckSF        0.00
PavedDrive        0.00
Fireplaces        0.00
KitchenAbvGr      0.00
ExterCond         0.00
BedroomAbvGr      0.00
HalfBath          0.00
FullBath          0.00
GrLivArea         0.00
LowQualFinSF      0.00
2ndFlrSF          0.00
1stFlrSF          0.00
Electrical        0.00
CentralAir        0.00
HeatingQC         0.00
Foundation        0.00
Id                0.00
dtype: float64

## Method will return number of levels,null values,unique values,data types

def Observations(df):
    return(pd.DataFrame({'dtypes' : df.dtypes,
                         'levels' : [df[x].unique() for x in df.columns],
                         'null_values' : df.isna().sum(),
                         'Unique Values': df.nunique()
                        }))

## Get columns data types,number of leveel,null values,unique value for each column of train data.
Observations(data)

## Get columns data types,number of leveel,null values,unique value for each column of test data.
Observations(test_data)

## Check dimensionns of train data.
data.shape

(1460, 81)

## Check dimesnions of test data.
test_data.shape

(1459, 80)

### change nan calumn names to other
#data.Alley.replace(to_replace=dict(nan='NAC'), inplace=True)

#data.Alley.replace(['NaN'], ['NAC'], inplace=True)

#data.Alley[data.Alley == 'nan'] = 'NAC'

#data.Alley.replace(to_replace ="nan", value ="NAC", inplace=True)

## I am replacing NA to NAA for Alley column of train data,bcz NA is having different levels meaning.
data.Alley.fillna('NAA',inplace=True)

## I am replacing NA to NAA for Alley column of test data,bcz NA is having different levels meaning.
test_data.Alley.fillna('NAA',inplace=True)

#data.Alley.replace(to_replace = np.nan, value ='NAC', inplace=True)

## Check unique values for Alley column of train data.
data.Alley.unique()

array(['NAA', 'Grvl', 'Pave'], dtype=object)

## Check unique values for Alley column of test data.
test_data.Alley.unique()

array(['NAA', 'Pave', 'Grvl'], dtype=object)

## Check first 5 records of train data.
data.head()

## Fill NAs with NB value for BsmtQual column of train data.
#data.BsmtQual.replace(to_replace = np.nan, value ='NB', inplace=True)
data.BsmtQual.fillna('NB',inplace=True)

## Fill NAs with NB value for BsmtQual column of test data.
test_data.BsmtQual.fillna('NB',inplace=True)

## Fill NAs with NB value for BsmtCond column of train data.
#data.BsmtCond.replace(to_replace = np.nan, value ='NB', inplace=True)
data.BsmtCond.fillna('NB',inplace=True)

## Fill NAs with NB value for BsmtCond column of test data.
test_data.BsmtCond.fillna('NB',inplace=True)

## Fill NAs with NB value for BsmtExposure column of train data.
#data.BsmtExposure.replace(to_replace = np.nan, value ='NB', inplace=True)
data.BsmtExposure.fillna('NB',inplace=True)

## Fill NAs with NB value for BsmtExposure column of test data.
test_data.BsmtExposure.fillna('NB',inplace=True)

## Fill NAs with NB value for BsmtFinType1 column of train data.
#data.BsmtFinType1.replace(to_replace = np.nan, value ='NB', inplace=True)
data.BsmtFinType1.fillna('NB',inplace=True)

## Fill NAs with NB value for BsmtFinType1 column of test data.
test_data.BsmtFinType1.fillna('NB',inplace=True)

## Fill NAs with NB value for BsmtFinType2 column of train data.
#data.BsmtFinType2.replace(to_replace = np.nan, value ='NB', inplace=True)
data.BsmtFinType2.fillna('NB',inplace=True)

## Fill NAs with NB value for BsmtFinType2 column of test data.
test_data.BsmtFinType2.fillna('NB',inplace=True)

## Fill NAs with NF value for FireplaceQu column of train data.
#data.FireplaceQu.replace(to_replace = np.nan, value ='NF', inplace=True)
data.FireplaceQu.fillna('NF',inplace=True)

## Fill NAs with NF value for FireplaceQu column of test data.
test_data.FireplaceQu.fillna('NF',inplace=True)

## Fill NAs with NG value for GarageType column of train data.
data.GarageType.fillna('NG',inplace=True)

## Fill NAs with NG value for GarageType column of test data.
test_data.GarageType.fillna('NG',inplace=True)

## Fill NAs with NG value for GarageFinish column of train data.
data.GarageFinish.fillna('NG',inplace=True)

## Fill NAs with NG value for GarageFinish column of test data.
test_data.GarageFinish.fillna('NG',inplace=True)

## Fill NAs with NG value for GarageQual column of train data.
data.GarageQual.fillna('NG',inplace=True)

## Fill NAs with NG value for GarageQual column of test data.
test_data.GarageQual.fillna('NG',inplace=True)

## Fill NAs with NG value for GarageCond column of train data.
data.GarageCond.fillna('NG',inplace=True)

## Fill NAs with NG value for GarageCond column of test data.
test_data.GarageCond.fillna('NG',inplace=True)

## Fill NAs with NP value for PoolQC column of train data.
data.PoolQC.fillna('NP',inplace=True)

## Fill NAs with NP value for PoolQC column of test data.
test_data.PoolQC.fillna('NP',inplace=True)

## Fill NAs with NF value for Fence column of train data.
data.Fence.fillna('NF',inplace=True)

## Fill NAs with NF value for Fence column of test data.
test_data.Fence.fillna('NF',inplace=True)

## Fill NAs with NE value for MiscFeature column of train data.
data.MiscFeature.fillna('NE',inplace=True)

## Fill NAs with NE value for MiscFeature column of test data.
test_data.MiscFeature.fillna('NE',inplace=True)

## Display first record of train data.
data[:1]

## Display first record of test data.
test_data[:1]

## Get summary statistics of train data.
Observations(data)

## Get summary statistics of test data.
Observations(test_data)

## Get data type of MSZoning column.
data.MSZoning.dtypes

dtype('O')

## Fetch category,object data types columns from train data.
object_columns = data.select_dtypes(include=['object','category'])

## Fetch category,object data types columns from test data.
test_object_columns = test_data.select_dtypes(include=['object','category'])

## Display first records of category & object columns of train data.
object_columns.head()

## Display first records of category & object columns of test data.
test_object_columns.head()

## Convert object to category data type.
for col in object_columns.columns:
    data[col] = data[col].astype('str').astype('category')

## Get columns data types of train data.
data.dtypes

Id                  int64
MSSubClass          int64
MSZoning         category
LotFrontage       float64
LotArea             int64
Street           category
Alley            category
LotShape         category
LandContour      category
Utilities        category
LotConfig        category
LandSlope        category
Neighborhood     category
Condition1       category
Condition2       category
BldgType         category
HouseStyle       category
OverallQual         int64
OverallCond         int64
YearBuilt           int64
YearRemodAdd        int64
RoofStyle        category
RoofMatl         category
Exterior1st      category
Exterior2nd      category
MasVnrType       category
MasVnrArea        float64
ExterQual        category
ExterCond        category
Foundation       category
BsmtQual         category
BsmtCond         category
BsmtExposure     category
BsmtFinType1     category
BsmtFinSF1          int64
BsmtFinType2     category
BsmtFinSF2          int64
BsmtUnfSF           int64
TotalBsmtSF         int64
Heating          category
HeatingQC        category
CentralAir       category
Electrical       category
1stFlrSF            int64
2ndFlrSF            int64
LowQualFinSF        int64
GrLivArea           int64
BsmtFullBath        int64
BsmtHalfBath        int64
FullBath            int64
HalfBath            int64
BedroomAbvGr        int64
KitchenAbvGr        int64
KitchenQual      category
TotRmsAbvGrd        int64
Functional       category
Fireplaces          int64
FireplaceQu      category
GarageType       category
GarageYrBlt       float64
GarageFinish     category
GarageCars          int64
GarageArea          int64
GarageQual       category
GarageCond       category
PavedDrive       category
WoodDeckSF          int64
OpenPorchSF         int64
EnclosedPorch       int64
3SsnPorch           int64
ScreenPorch         int64
PoolArea            int64
PoolQC           category
Fence            category
MiscFeature      category
MiscVal             int64
MoSold              int64
YrSold              int64
SaleType         category
SaleCondition    category
SalePrice           int64
dtype: object

## Convert object data typpe to category.
for col in test_object_columns.columns:
    test_data[col] = test_data[col].astype('str').astype('category')

## Get column data types of test data.
test_data.dtypes

Id                  int64
MSSubClass          int64
MSZoning         category
LotFrontage       float64
LotArea             int64
Street           category
Alley            category
LotShape         category
LandContour      category
Utilities        category
LotConfig        category
LandSlope        category
Neighborhood     category
Condition1       category
Condition2       category
BldgType         category
HouseStyle       category
OverallQual         int64
OverallCond         int64
YearBuilt           int64
YearRemodAdd        int64
RoofStyle        category
RoofMatl         category
Exterior1st      category
Exterior2nd      category
MasVnrType       category
MasVnrArea        float64
ExterQual        category
ExterCond        category
Foundation       category
BsmtQual         category
BsmtCond         category
BsmtExposure     category
BsmtFinType1     category
BsmtFinSF1        float64
BsmtFinType2     category
BsmtFinSF2        float64
BsmtUnfSF         float64
TotalBsmtSF       float64
Heating          category
HeatingQC        category
CentralAir       category
Electrical       category
1stFlrSF            int64
2ndFlrSF            int64
LowQualFinSF        int64
GrLivArea           int64
BsmtFullBath      float64
BsmtHalfBath      float64
FullBath            int64
HalfBath            int64
BedroomAbvGr        int64
KitchenAbvGr        int64
KitchenQual      category
TotRmsAbvGrd        int64
Functional       category
Fireplaces          int64
FireplaceQu      category
GarageType       category
GarageYrBlt       float64
GarageFinish     category
GarageCars        float64
GarageArea        float64
GarageQual       category
GarageCond       category
PavedDrive       category
WoodDeckSF          int64
OpenPorchSF         int64
EnclosedPorch       int64
3SsnPorch           int64
ScreenPorch         int64
PoolArea            int64
PoolQC           category
Fence            category
MiscFeature      category
MiscVal             int64
MoSold              int64
YrSold              int64
SaleType         category
SaleCondition    category
dtype: object

## Convert numeric columns into categorical varibles
cols = ['MSSubClass','OverallQual','OverallCond']
for col in cols:
    data[col] = data[col].astype('str').astype('category')

## Convert numeric data types to categorical data types.
for col in cols:
    test_data[col] = test_data[col].astype('str').astype('category')

## Get columns data types of train data.
data.dtypes

Id                  int64
MSSubClass       category
MSZoning         category
LotFrontage       float64
LotArea             int64
Street           category
Alley            category
LotShape         category
LandContour      category
Utilities        category
LotConfig        category
LandSlope        category
Neighborhood     category
Condition1       category
Condition2       category
BldgType         category
HouseStyle       category
OverallQual      category
OverallCond      category
YearBuilt           int64
YearRemodAdd        int64
RoofStyle        category
RoofMatl         category
Exterior1st      category
Exterior2nd      category
MasVnrType       category
MasVnrArea        float64
ExterQual        category
ExterCond        category
Foundation       category
BsmtQual         category
BsmtCond         category
BsmtExposure     category
BsmtFinType1     category
BsmtFinSF1          int64
BsmtFinType2     category
BsmtFinSF2          int64
BsmtUnfSF           int64
TotalBsmtSF         int64
Heating          category
HeatingQC        category
CentralAir       category
Electrical       category
1stFlrSF            int64
2ndFlrSF            int64
LowQualFinSF        int64
GrLivArea           int64
BsmtFullBath        int64
BsmtHalfBath        int64
FullBath            int64
HalfBath            int64
BedroomAbvGr        int64
KitchenAbvGr        int64
KitchenQual      category
TotRmsAbvGrd        int64
Functional       category
Fireplaces          int64
FireplaceQu      category
GarageType       category
GarageYrBlt       float64
GarageFinish     category
GarageCars          int64
GarageArea          int64
GarageQual       category
GarageCond       category
PavedDrive       category
WoodDeckSF          int64
OpenPorchSF         int64
EnclosedPorch       int64
3SsnPorch           int64
ScreenPorch         int64
PoolArea            int64
PoolQC           category
Fence            category
MiscFeature      category
MiscVal             int64
MoSold              int64
YrSold              int64
SaleType         category
SaleCondition    category
SalePrice           int64
dtype: object

## Get columns data types of test data.
test_data.dtypes

Id                  int64
MSSubClass       category
MSZoning         category
LotFrontage       float64
LotArea             int64
Street           category
Alley            category
LotShape         category
LandContour      category
Utilities        category
LotConfig        category
LandSlope        category
Neighborhood     category
Condition1       category
Condition2       category
BldgType         category
HouseStyle       category
OverallQual      category
OverallCond      category
YearBuilt           int64
YearRemodAdd        int64
RoofStyle        category
RoofMatl         category
Exterior1st      category
Exterior2nd      category
MasVnrType       category
MasVnrArea        float64
ExterQual        category
ExterCond        category
Foundation       category
BsmtQual         category
BsmtCond         category
BsmtExposure     category
BsmtFinType1     category
BsmtFinSF1        float64
BsmtFinType2     category
BsmtFinSF2        float64
BsmtUnfSF         float64
TotalBsmtSF       float64
Heating          category
HeatingQC        category
CentralAir       category
Electrical       category
1stFlrSF            int64
2ndFlrSF            int64
LowQualFinSF        int64
GrLivArea           int64
BsmtFullBath      float64
BsmtHalfBath      float64
FullBath            int64
HalfBath            int64
BedroomAbvGr        int64
KitchenAbvGr        int64
KitchenQual      category
TotRmsAbvGrd        int64
Functional       category
Fireplaces          int64
FireplaceQu      category
GarageType       category
GarageYrBlt       float64
GarageFinish     category
GarageCars        float64
GarageArea        float64
GarageQual       category
GarageCond       category
PavedDrive       category
WoodDeckSF          int64
OpenPorchSF         int64
EnclosedPorch       int64
3SsnPorch           int64
ScreenPorch         int64
PoolArea            int64
PoolQC           category
Fence            category
MiscFeature      category
MiscVal             int64
MoSold              int64
YrSold              int64
SaleType         category
SaleCondition    category
dtype: object

## Set index for train data and display first 5 records.
data = data.set_index('Id')
data.head()

## Set index for test data and display first 5 records.
test_data = test_data.set_index('Id')
test_data.head()

## Seperate numeric and categorical columns for train data.
cat_columns = data.select_dtypes(include=['category'])
num_columns = data.select_dtypes(include=['int64', 'float64'])

## Seperate numeric and categorical columns for test data.
test_cat_columns = test_data.select_dtypes(include=['category'])
test_num_columns = test_data.select_dtypes(include=['int64', 'float64'])

## Get unique values for BsmtFullBath column of train data.
data.BsmtFullBath.nunique()

4

## Below logic is used for checking special charcter in numeric columns(Train data).

for col in num_columns.columns:
    
    print('\n',col,'----->')
    for index in range(1,len(data)):
        try:
            skip=float(data.loc[index,col])
            skip=int(data.loc[index,col])
        except ValueError :
            print(index,data.loc[index,col])

 LotFrontage ----->
8 nan
13 nan
15 nan
17 nan
25 nan
32 nan
43 nan
44 nan
51 nan
65 nan
67 nan
77 nan
85 nan
96 nan
101 nan
105 nan
112 nan
114 nan
117 nan
121 nan
127 nan
132 nan
134 nan
137 nan
148 nan
150 nan
153 nan
154 nan
161 nan
167 nan
170 nan
171 nan
178 nan
181 nan
187 nan
192 nan
204 nan
208 nan
209 nan
215 nan
219 nan
222 nan
235 nan
238 nan
245 nan
250 nan
270 nan
288 nan
289 nan
294 nan
308 nan
309 nan
311 nan
320 nan
329 nan
331 nan
336 nan
343 nan
347 nan
348 nan
352 nan
357 nan
361 nan
362 nan
365 nan
367 nan
370 nan
371 nan
376 nan
385 nan
393 nan
394 nan
405 nan
406 nan
413 nan
422 nan
427 nan
448 nan
453 nan
458 nan
459 nan
460 nan
466 nan
471 nan
485 nan
491 nan
497 nan
517 nan
519 nan
530 nan
538 nan
539 nan
540 nan
542 nan
546 nan
560 nan
561 nan
565 nan
570 nan
581 nan
594 nan
611 nan
612 nan
613 nan
617 nan
624 nan
627 nan
642 nan
646 nan
661 nan
667 nan
669 nan
673 nan
680 nan
683 nan
686 nan
688 nan
691 nan
707 nan
710 nan
715 nan
721 nan
722 nan
727 nan
735 nan
746 nan
747 nan
752 nan
758 nan
771 nan
784 nan
786 nan
790 nan
792 nan
795 nan
812 nan
817 nan
818 nan
823 nan
829 nan
841 nan
846 nan
852 nan
854 nan
856 nan
857 nan
860 nan
866 nan
869 nan
880 nan
883 nan
894 nan
901 nan
905 nan
909 nan
912 nan
918 nan
926 nan
928 nan
929 nan
930 nan
940 nan
942 nan
945 nan
954 nan
962 nan
968 nan
976 nan
981 nan
984 nan
989 nan
997 nan
998 nan
1004 nan
1007 nan
1018 nan
1019 nan
1025 nan
1031 nan
1033 nan
1034 nan
1036 nan
1038 nan
1042 nan
1046 nan
1058 nan
1060 nan
1065 nan
1078 nan
1085 nan
1087 nan
1098 nan
1109 nan
1111 nan
1117 nan
1123 nan
1125 nan
1139 nan
1142 nan
1144 nan
1147 nan
1149 nan
1154 nan
1155 nan
1162 nan
1165 nan
1178 nan
1181 nan
1191 nan
1194 nan
1207 nan
1214 nan
1231 nan
1234 nan
1245 nan
1248 nan
1252 nan
1254 nan
1261 nan
1263 nan
1269 nan
1271 nan
1272 nan
1273 nan
1277 nan
1278 nan
1287 nan
1288 nan
1291 nan
1301 nan
1302 nan
1310 nan
1313 nan
1319 nan
1322 nan
1343 nan
1347 nan
1349 nan
1355 nan
1357 nan
1358 nan
1359 nan
1363 nan
1366 nan
1369 nan
1374 nan
1382 nan
1384 nan
1397 nan
1408 nan
1418 nan
1420 nan
1424 nan
1425 nan
1430 nan
1432 nan
1442 nan
1444 nan
1447 nan

 LotArea ----->

 YearBuilt ----->

 YearRemodAdd ----->

 MasVnrArea ----->
235 nan
530 nan
651 nan
937 nan
974 nan
978 nan
1244 nan
1279 nan

 BsmtFinSF1 ----->

 BsmtFinSF2 ----->

 BsmtUnfSF ----->

 TotalBsmtSF ----->

 1stFlrSF ----->

 2ndFlrSF ----->

 LowQualFinSF ----->

 GrLivArea ----->

 BsmtFullBath ----->

 BsmtHalfBath ----->

 FullBath ----->

 HalfBath ----->

 BedroomAbvGr ----->

 KitchenAbvGr ----->

 TotRmsAbvGrd ----->

 Fireplaces ----->

 GarageYrBlt ----->
40 nan
49 nan
79 nan
89 nan
90 nan
100 nan
109 nan
126 nan
128 nan
141 nan
149 nan
156 nan
164 nan
166 nan
199 nan
211 nan
242 nan
251 nan
288 nan
292 nan
308 nan
376 nan
387 nan
394 nan
432 nan
435 nan
442 nan
465 nan
496 nan
521 nan
529 nan
534 nan
536 nan
563 nan
583 nan
614 nan
615 nan
621 nan
636 nan
637 nan
639 nan
650 nan
706 nan
711 nan
739 nan
751 nan
785 nan
827 nan
844 nan
922 nan
943 nan
955 nan
961 nan
969 nan
971 nan
977 nan
1010 nan
1012 nan
1031 nan
1039 nan
1097 nan
1124 nan
1132 nan
1138 nan
1144 nan
1174 nan
1180 nan
1219 nan
1220 nan
1235 nan
1258 nan
1284 nan
1324 nan
1326 nan
1327 nan
1338 nan
1350 nan
1408 nan
1450 nan
1451 nan
1454 nan

 GarageCars ----->

 GarageArea ----->

 WoodDeckSF ----->

 OpenPorchSF ----->

 EnclosedPorch ----->

 3SsnPorch ----->

 ScreenPorch ----->

 PoolArea ----->

 MiscVal ----->

 MoSold ----->

 YrSold ----->

 SalePrice ----->

## Display train data numeric columns.
num_columns.columns

Index(['LotFrontage', 'LotArea', 'YearBuilt', 'YearRemodAdd', 'MasVnrArea',
       'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', '1stFlrSF',
       '2ndFlrSF', 'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath',
       'FullBath', 'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'TotRmsAbvGrd',
       'Fireplaces', 'GarageYrBlt', 'GarageCars', 'GarageArea', 'WoodDeckSF',
       'OpenPorchSF', 'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea',
       'MiscVal', 'MoSold', 'YrSold', 'SalePrice'],
      dtype='object')

## Display test data numeric columns.
test_num_columns.columns

Index(['LotFrontage', 'LotArea', 'YearBuilt', 'YearRemodAdd', 'MasVnrArea',
       'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', '1stFlrSF',
       '2ndFlrSF', 'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath',
       'FullBath', 'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'TotRmsAbvGrd',
       'Fireplaces', 'GarageYrBlt', 'GarageCars', 'GarageArea', 'WoodDeckSF',
       'OpenPorchSF', 'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea',
       'MiscVal', 'MoSold', 'YrSold'],
      dtype='object')

## Check corrlation between numeric columns of train data.
data[num_columns.columns].corr()

## Check corrlation between numeric columns of test data.
test_data[test_num_columns.columns].corr()

## Calculate variance for numeric columns.
def variance(x):
        return(pd.DataFrame({'Datatype' : x.dtypes,
                            'Variance': [round(x[i].var()) for i in x]
                            }))

## Get variance for numeric columns of train data.
variance(num_columns)

## Get variance for numeric columns of test data.
variance(test_num_columns)

## Drop zero variance variable from train data set.
cols = ['BsmtFullBath','BsmtHalfBath','FullBath','HalfBath','KitchenAbvGr','Fireplaces']
data = data.drop(cols,axis=1)
num_columns = num_columns.drop(cols,axis=1)

## Drop zero variance variable from test data set.
test_data = test_data.drop(cols,axis=1)
test_num_columns = test_num_columns.drop(cols,axis=1)

## Get first record of train data after dropping few columns.
data[:1]

## Get first record of test data after dropping few columns.
test_data[:1]

## Seperate Target and Predictors and display dimensions.

features = data.drop('SalePrice', axis = 1)
print(features.shape)
target = data['SalePrice']
print(target.shape)

(1460, 73)
(1460,)

## Display dimesnions of test data.
test_data.shape

(1459, 73)

## Split data into train and validation.
X_train,X_test,y_train,y_test=train_test_split(features,target,test_size=0.3,random_state=123)

## Seperate category and numeric  columns for train data.
catcols_train = X_train.select_dtypes(include=['object','category'])
numcols_train = X_train.select_dtypes(include=['int64', 'float64'])

## Seperate category and numeric  columns for test data.
test_catcols = test_data.select_dtypes(include=['object','category'])
test_numcols = test_data.select_dtypes(include=['int64', 'float64'])

## Display dimensions and column names of category and numeric columns of train data.
print(catcols_train.shape)
print(catcols_train.columns)
print(numcols_train.shape)
print(numcols_train.columns)

(1022, 46)
Index(['MSSubClass', 'MSZoning', 'Street', 'Alley', 'LotShape', 'LandContour',
       'Utilities', 'LotConfig', 'LandSlope', 'Neighborhood', 'Condition1',
       'Condition2', 'BldgType', 'HouseStyle', 'OverallQual', 'OverallCond',
       'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType',
       'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual', 'BsmtCond',
       'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2', 'Heating', 'HeatingQC',
       'CentralAir', 'Electrical', 'KitchenQual', 'Functional', 'FireplaceQu',
       'GarageType', 'GarageFinish', 'GarageQual', 'GarageCond', 'PavedDrive',
       'PoolQC', 'Fence', 'MiscFeature', 'SaleType', 'SaleCondition'],
      dtype='object')
(1022, 27)
Index(['LotFrontage', 'LotArea', 'YearBuilt', 'YearRemodAdd', 'MasVnrArea',
       'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', '1stFlrSF',
       '2ndFlrSF', 'LowQualFinSF', 'GrLivArea', 'BedroomAbvGr', 'TotRmsAbvGrd',
       'GarageYrBlt', 'GarageCars', 'GarageArea', 'WoodDeckSF', 'OpenPorchSF',
       'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'MiscVal',
       'MoSold', 'YrSold'],
      dtype='object')

## Display dimensions and column names of category and numeric columns of validation data.
print(test_catcols.shape)
print(test_catcols.columns)
print(test_numcols.shape)
print(test_numcols.columns)

(1459, 46)
Index(['MSSubClass', 'MSZoning', 'Street', 'Alley', 'LotShape', 'LandContour',
       'Utilities', 'LotConfig', 'LandSlope', 'Neighborhood', 'Condition1',
       'Condition2', 'BldgType', 'HouseStyle', 'OverallQual', 'OverallCond',
       'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType',
       'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual', 'BsmtCond',
       'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2', 'Heating', 'HeatingQC',
       'CentralAir', 'Electrical', 'KitchenQual', 'Functional', 'FireplaceQu',
       'GarageType', 'GarageFinish', 'GarageQual', 'GarageCond', 'PavedDrive',
       'PoolQC', 'Fence', 'MiscFeature', 'SaleType', 'SaleCondition'],
      dtype='object')
(1459, 27)
Index(['LotFrontage', 'LotArea', 'YearBuilt', 'YearRemodAdd', 'MasVnrArea',
       'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', '1stFlrSF',
       '2ndFlrSF', 'LowQualFinSF', 'GrLivArea', 'BedroomAbvGr', 'TotRmsAbvGrd',
       'GarageYrBlt', 'GarageCars', 'GarageArea', 'WoodDeckSF', 'OpenPorchSF',
       'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'MiscVal',
       'MoSold', 'YrSold'],
      dtype='object')

## Seperate category and numeric columns from test data.
catcols_test = X_test.select_dtypes(include=['object','category'])
numcols_test = X_test.select_dtypes(include=['int64', 'float64'])

## Display dimesniosn of test data.
catcols_test.shape

(438, 46)

################################################### Imputation ###############################################################

## Import imputer,scaler libraries for imputing null values.
from sklearn.impute import SimpleImputer

## Instantiate numeric ,category imputers.
num_imputer = SimpleImputer(strategy = 'median')
cat_imputer = SimpleImputer(strategy = 'most_frequent')

## Fit numeric imputer.
num_imputer.fit(numcols_train)

## Impute numeric columns NA values of train data and prepare data frame.
X_train_imp = num_imputer.transform(numcols_train)
X_train_imp =pd.DataFrame(X_train_imp,columns=numcols_train.columns)

## Check NA values for numeric columns of train data after imputing.
X_train_imp.isna().sum()

LotFrontage      0
LotArea          0
YearBuilt        0
YearRemodAdd     0
MasVnrArea       0
BsmtFinSF1       0
BsmtFinSF2       0
BsmtUnfSF        0
TotalBsmtSF      0
1stFlrSF         0
2ndFlrSF         0
LowQualFinSF     0
GrLivArea        0
BedroomAbvGr     0
TotRmsAbvGrd     0
GarageYrBlt      0
GarageCars       0
GarageArea       0
WoodDeckSF       0
OpenPorchSF      0
EnclosedPorch    0
3SsnPorch        0
ScreenPorch      0
PoolArea         0
MiscVal          0
MoSold           0
YrSold           0
dtype: int64

## Check first 5 records of numeric column of train data.
X_train_imp.head()

## Fit category imputer.
cat_imputer.fit(catcols_train)

## Impute NA values for category columns of train data and prepares a dataframe.
X_train_imp_cat = cat_imputer.transform(catcols_train)
X_train_imp_cat = pd.DataFrame(X_train_imp_cat,columns=catcols_train.columns)

## Check dimesnions of category columns of train data.
X_train_imp_cat.shape

(1022, 46)

## Check NA values for category columns of train data after imputation.
X_train_imp_cat.isna().sum()

MSSubClass       0
MSZoning         0
Street           0
Alley            0
LotShape         0
LandContour      0
Utilities        0
LotConfig        0
LandSlope        0
Neighborhood     0
Condition1       0
Condition2       0
BldgType         0
HouseStyle       0
OverallQual      0
OverallCond      0
RoofStyle        0
RoofMatl         0
Exterior1st      0
Exterior2nd      0
MasVnrType       0
ExterQual        0
ExterCond        0
Foundation       0
BsmtQual         0
BsmtCond         0
BsmtExposure     0
BsmtFinType1     0
BsmtFinType2     0
Heating          0
HeatingQC        0
CentralAir       0
Electrical       0
KitchenQual      0
Functional       0
FireplaceQu      0
GarageType       0
GarageFinish     0
GarageQual       0
GarageCond       0
PavedDrive       0
PoolQC           0
Fence            0
MiscFeature      0
SaleType         0
SaleCondition    0
dtype: int64

## Check first 5 records of category columns of train data.
X_train_imp_cat.head()

## Impute NA values for numeric columns of validation data and prepares a dataframe,display first 5 records.
X_test_imp = num_imputer.transform(numcols_test)
X_test_imp =pd.DataFrame(X_test_imp,columns=numcols_test.columns)
X_test_imp.head()

## Check NA values for numeric columns of validation data after imputing.
X_test_imp.isna().sum()

LotFrontage      0
LotArea          0
YearBuilt        0
YearRemodAdd     0
MasVnrArea       0
BsmtFinSF1       0
BsmtFinSF2       0
BsmtUnfSF        0
TotalBsmtSF      0
1stFlrSF         0
2ndFlrSF         0
LowQualFinSF     0
GrLivArea        0
BedroomAbvGr     0
TotRmsAbvGrd     0
GarageYrBlt      0
GarageCars       0
GarageArea       0
WoodDeckSF       0
OpenPorchSF      0
EnclosedPorch    0
3SsnPorch        0
ScreenPorch      0
PoolArea         0
MiscVal          0
MoSold           0
YrSold           0
dtype: int64

## Impute NA values for category columns of validation data and prepares a dataframe and display first 5 records.
X_test_imp_cat = cat_imputer.transform(catcols_test)
X_test_imp_cat = pd.DataFrame(X_test_imp_cat,columns=catcols_test.columns)
X_test_imp_cat.head()

## Display dimensions of category columns of validation data.
X_test_imp_cat.shape

(438, 46)

## Check NA values for category columns of validation data after imputation.
X_test_imp_cat.isna().sum()

MSSubClass       0
MSZoning         0
Street           0
Alley            0
LotShape         0
LandContour      0
Utilities        0
LotConfig        0
LandSlope        0
Neighborhood     0
Condition1       0
Condition2       0
BldgType         0
HouseStyle       0
OverallQual      0
OverallCond      0
RoofStyle        0
RoofMatl         0
Exterior1st      0
Exterior2nd      0
MasVnrType       0
ExterQual        0
ExterCond        0
Foundation       0
BsmtQual         0
BsmtCond         0
BsmtExposure     0
BsmtFinType1     0
BsmtFinType2     0
Heating          0
HeatingQC        0
CentralAir       0
Electrical       0
KitchenQual      0
Functional       0
FireplaceQu      0
GarageType       0
GarageFinish     0
GarageQual       0
GarageCond       0
PavedDrive       0
PoolQC           0
Fence            0
MiscFeature      0
SaleType         0
SaleCondition    0
dtype: int64

## Impute numeric columns NA values of test data and prepare data frame,display first 5 records.
test_imp = num_imputer.transform(test_numcols)
test_imp =pd.DataFrame(test_imp,columns=test_numcols.columns)
test_imp.head()

## Check NA values for numeric columns of test data after imputing.
test_imp.isna().sum()

LotFrontage      0
LotArea          0
YearBuilt        0
YearRemodAdd     0
MasVnrArea       0
BsmtFinSF1       0
BsmtFinSF2       0
BsmtUnfSF        0
TotalBsmtSF      0
1stFlrSF         0
2ndFlrSF         0
LowQualFinSF     0
GrLivArea        0
BedroomAbvGr     0
TotRmsAbvGrd     0
GarageYrBlt      0
GarageCars       0
GarageArea       0
WoodDeckSF       0
OpenPorchSF      0
EnclosedPorch    0
3SsnPorch        0
ScreenPorch      0
PoolArea         0
MiscVal          0
MoSold           0
YrSold           0
dtype: int64

## Impute NA values for category columns of test data and prepares a dataframe,display first 5 records.
test_imp_cat = cat_imputer.transform(test_catcols)
test_imp_cat = pd.DataFrame(test_imp_cat,columns=test_catcols.columns)
test_imp_cat.head()

## Display dimensions of category columns of test data.
test_imp_cat.shape

(1459, 46)

## Check NA values for category columns of test data after imputation.
test_imp_cat.isna().sum()

MSSubClass       0
MSZoning         0
Street           0
Alley            0
LotShape         0
LandContour      0
Utilities        0
LotConfig        0
LandSlope        0
Neighborhood     0
Condition1       0
Condition2       0
BldgType         0
HouseStyle       0
OverallQual      0
OverallCond      0
RoofStyle        0
RoofMatl         0
Exterior1st      0
Exterior2nd      0
MasVnrType       0
ExterQual        0
ExterCond        0
Foundation       0
BsmtQual         0
BsmtCond         0
BsmtExposure     0
BsmtFinType1     0
BsmtFinType2     0
Heating          0
HeatingQC        0
CentralAir       0
Electrical       0
KitchenQual      0
Functional       0
FireplaceQu      0
GarageType       0
GarageFinish     0
GarageQual       0
GarageCond       0
PavedDrive       0
PoolQC           0
Fence            0
MiscFeature      0
SaleType         0
SaleCondition    0
dtype: int64

#################################################### Standardization  ##########################################################

## Import Scaler library to scale the numeric values.
from sklearn.preprocessing import StandardScaler

## Instantiate scaler and fit a model.
scaler = StandardScaler()
scaler.fit(X_train_imp)

StandardScaler(copy=True, with_mean=True, with_std=True)

## Standardize numeric column values of train data,prepare a dataframe and display first 5 records.
X_train_scaler = scaler.transform(X_train_imp)
X_train_scaler = pd.DataFrame(X_train_scaler,columns=X_train_imp.columns)
X_train_scaler.head()

## Standardize numeric column values of validation data,prepare a dataframe and display first 5 records.
X_test_scaler = scaler.transform(X_test_imp)
X_test_scaler = pd.DataFrame(X_test_scaler,columns=X_test_imp.columns)
X_test_scaler.head()

## Standardize numeric column values of test data,prepare a dataframe and display first 5 records.
test_scaler = scaler.transform(test_imp)
test_scaler = pd.DataFrame(test_scaler,columns=test_imp.columns)
test_scaler.head()

## Combine numeric  and category columns of train data and display dimesions of result dataframe.
train_result = ""
train_result = pd.concat([X_train_scaler, X_train_imp_cat], axis=1)
train_result.shape

(1022, 73)

## Check first 5 records of train data.
train_result.head()

## Check NA values for train data.
train_result.isna().sum()

LotFrontage      0
LotArea          0
YearBuilt        0
YearRemodAdd     0
MasVnrArea       0
BsmtFinSF1       0
BsmtFinSF2       0
BsmtUnfSF        0
TotalBsmtSF      0
1stFlrSF         0
2ndFlrSF         0
LowQualFinSF     0
GrLivArea        0
BedroomAbvGr     0
TotRmsAbvGrd     0
GarageYrBlt      0
GarageCars       0
GarageArea       0
WoodDeckSF       0
OpenPorchSF      0
EnclosedPorch    0
3SsnPorch        0
ScreenPorch      0
PoolArea         0
MiscVal          0
MoSold           0
YrSold           0
MSSubClass       0
MSZoning         0
Street           0
Alley            0
LotShape         0
LandContour      0
Utilities        0
LotConfig        0
LandSlope        0
Neighborhood     0
Condition1       0
Condition2       0
BldgType         0
HouseStyle       0
OverallQual      0
OverallCond      0
RoofStyle        0
RoofMatl         0
Exterior1st      0
Exterior2nd      0
MasVnrType       0
ExterQual        0
ExterCond        0
Foundation       0
BsmtQual         0
BsmtCond         0
BsmtExposure     0
BsmtFinType1     0
BsmtFinType2     0
Heating          0
HeatingQC        0
CentralAir       0
Electrical       0
KitchenQual      0
Functional       0
FireplaceQu      0
GarageType       0
GarageFinish     0
GarageQual       0
GarageCond       0
PavedDrive       0
PoolQC           0
Fence            0
MiscFeature      0
SaleType         0
SaleCondition    0
dtype: int64

## Prepare a dataframe with train data.
dataframe1 = pd.DataFrame(train_result)

## Copy dataframe data into a CSV file.
dataframe1.to_csv('TrainDataPreprocess.csv',index=False)

## Combine numeric  and category columns of validation data.
test_result = ""
test_result = pd.concat([X_test_scaler, X_test_imp_cat], axis=1)

## Check dimesions of validation data.
test_result.shape

(438, 73)

## Get first 5 records of validation data.
test_result.head()

## Check NA values for validation data after combining numeric and category columns.
test_result.isna().sum()

LotFrontage      0
LotArea          0
YearBuilt        0
YearRemodAdd     0
MasVnrArea       0
BsmtFinSF1       0
BsmtFinSF2       0
BsmtUnfSF        0
TotalBsmtSF      0
1stFlrSF         0
2ndFlrSF         0
LowQualFinSF     0
GrLivArea        0
BedroomAbvGr     0
TotRmsAbvGrd     0
GarageYrBlt      0
GarageCars       0
GarageArea       0
WoodDeckSF       0
OpenPorchSF      0
EnclosedPorch    0
3SsnPorch        0
ScreenPorch      0
PoolArea         0
MiscVal          0
MoSold           0
YrSold           0
MSSubClass       0
MSZoning         0
Street           0
Alley            0
LotShape         0
LandContour      0
Utilities        0
LotConfig        0
LandSlope        0
Neighborhood     0
Condition1       0
Condition2       0
BldgType         0
HouseStyle       0
OverallQual      0
OverallCond      0
RoofStyle        0
RoofMatl         0
Exterior1st      0
Exterior2nd      0
MasVnrType       0
ExterQual        0
ExterCond        0
Foundation       0
BsmtQual         0
BsmtCond         0
BsmtExposure     0
BsmtFinType1     0
BsmtFinType2     0
Heating          0
HeatingQC        0
CentralAir       0
Electrical       0
KitchenQual      0
Functional       0
FireplaceQu      0
GarageType       0
GarageFinish     0
GarageQual       0
GarageCond       0
PavedDrive       0
PoolQC           0
Fence            0
MiscFeature      0
SaleType         0
SaleCondition    0
dtype: int64

## Prepare dataframe with validation data.
dataframe2 = pd.DataFrame(test_result)

## Copy dataframe data into a CSV file.
dataframe2.to_csv('ValidationDataPreprocess.csv',index=False)

## Prepare a dataframe with target column data of train.
dataframe3 = pd.DataFrame(y_train)

## Copy dataframe data into a CSV file.
dataframe3.to_csv('TrainTarget.csv',index=False)

## Prepare a dataframe with target column data of validation.
dataframe4 = pd.DataFrame(y_test)

## Copy dataframe data into a CSV file.
dataframe4.to_csv('ValidationTarget.csv',index=False)

################################################## Dummification###############################################################

## Display category column levels of train data.
for i in X_train_imp_cat:
    print(i , X_train_imp_cat[i].nunique())

MSSubClass 15
MSZoning 5
Street 2
Alley 3
LotShape 4
LandContour 4
Utilities 2
LotConfig 5
LandSlope 3
Neighborhood 25
Condition1 9
Condition2 8
BldgType 5
HouseStyle 8
OverallQual 10
OverallCond 9
RoofStyle 6
RoofMatl 7
Exterior1st 13
Exterior2nd 16
MasVnrType 5
ExterQual 4
ExterCond 5
Foundation 6
BsmtQual 5
BsmtCond 5
BsmtExposure 5
BsmtFinType1 7
BsmtFinType2 7
Heating 5
HeatingQC 5
CentralAir 2
Electrical 6
KitchenQual 4
Functional 6
FireplaceQu 6
GarageType 7
GarageFinish 4
GarageQual 6
GarageCond 6
PavedDrive 3
PoolQC 4
Fence 5
MiscFeature 4
SaleType 9
SaleCondition 6

## Display dimensions of train data.
X_train_imp_cat.shape

(1022, 46)

## Get dummies for category columns of train data,display dimesnionns and first 5 records.
catcols_train_dummy = pd.get_dummies(columns = X_train_imp_cat.columns, data = X_train_imp_cat, drop_first= True)
print(catcols_train_dummy.shape)
catcols_train_dummy.head()

(1022, 250)

## Display category column levels of validation data.
for i in X_test_imp_cat:
    print(i , X_test_imp_cat[i].nunique())

MSSubClass 15
MSZoning 4
Street 2
Alley 3
LotShape 4
LandContour 4
Utilities 1
LotConfig 5
LandSlope 3
Neighborhood 25
Condition1 8
Condition2 2
BldgType 5
HouseStyle 8
OverallQual 8
OverallCond 8
RoofStyle 5
RoofMatl 5
Exterior1st 14
Exterior2nd 14
MasVnrType 5
ExterQual 4
ExterCond 4
Foundation 6
BsmtQual 5
BsmtCond 4
BsmtExposure 5
BsmtFinType1 7
BsmtFinType2 7
Heating 5
HeatingQC 4
CentralAir 2
Electrical 4
KitchenQual 4
Functional 7
FireplaceQu 6
GarageType 7
GarageFinish 4
GarageQual 6
GarageCond 6
PavedDrive 3
PoolQC 3
Fence 5
MiscFeature 4
SaleType 6
SaleCondition 6

## Display dimesnions of category columns of validation data.
X_test_imp_cat.shape

(438, 46)

## Get dummies for category columns of train data,display dimesnionns and first 5 records.
catcols_test_dummy = pd.get_dummies(columns = X_test_imp_cat.columns, data = X_test_imp_cat, drop_first= True)
print(catcols_test_dummy.shape)
catcols_test_dummy.head()

(438, 226)

#train_levels,test_levels = catcols_train_dummy.align(catcols_test_dummy, join='outer', axis=1, fill_value=0)

## Get missing columns in the validation data test.
missing_cols = set( catcols_train_dummy.columns ) - set( catcols_test_dummy.columns )
## Add a missing column in test set with default value equal to 0.
for c in missing_cols:
    catcols_test_dummy[c] = 0
catcols_test_dummy = catcols_test_dummy[catcols_train_dummy.columns]

## Display dimensions of category columns of train data.
catcols_train_dummy.shape

(1022, 250)

## Display dimensions of category columns of validation data.
catcols_test_dummy.shape

(438, 250)

## Combine numeric and category columns of train data.
train_data_final = pd.concat([X_train_scaler, catcols_train_dummy], axis=1)

## Check dimesnions of train data.
train_data_final.shape

(1022, 277)

## Check dimesnions of target varible of train data.
y_train.shape

(1022,)

## Check first 5 records of train ata.
train_data_final.head()

## Combine numeric and category columns of validation data.
test_data_final = pd.concat([X_test_scaler, catcols_test_dummy], axis=1)

## Display dimensions of validation data.
test_data_final.shape

(438, 277)

## Get first 5 records of validation data.
test_data_final.head()

## Display category columns levels of test data.
for i in test_imp_cat:
    print(i , test_imp_cat[i].nunique())

MSSubClass 16
MSZoning 6
Street 2
Alley 3
LotShape 4
LandContour 4
Utilities 2
LotConfig 5
LandSlope 3
Neighborhood 25
Condition1 9
Condition2 5
BldgType 5
HouseStyle 7
OverallQual 10
OverallCond 9
RoofStyle 6
RoofMatl 4
Exterior1st 14
Exterior2nd 16
MasVnrType 5
ExterQual 4
ExterCond 5
Foundation 6
BsmtQual 5
BsmtCond 5
BsmtExposure 5
BsmtFinType1 7
BsmtFinType2 7
Heating 4
HeatingQC 5
CentralAir 2
Electrical 4
KitchenQual 5
Functional 8
FireplaceQu 6
GarageType 7
GarageFinish 4
GarageQual 5
GarageCond 6
PavedDrive 3
PoolQC 3
Fence 5
MiscFeature 4
SaleType 10
SaleCondition 6

## Check dimensions of category columns of test data before doing dummification.
test_imp_cat.shape

(1459, 46)

## Get dummies for category columns of test data,display dimesnionns and first 5 records.
test_catcols_dummy = pd.get_dummies(columns = test_imp_cat.columns, data = test_imp_cat, drop_first= True)
print(test_catcols_dummy.shape)
test_catcols_dummy.head()

(1459, 245)

## Get missing columns in the test data set.
missing_cols = set( catcols_train_dummy.columns ) - set( test_catcols_dummy.columns )
## Add a missing column in test set with default value equal to 0.
for c in missing_cols:
    test_catcols_dummy[c] = 0
test_catcols_dummy = test_catcols_dummy[catcols_train_dummy.columns]

## Check dimesnions of categoory columns of test data.
test_catcols_dummy.shape

(1459, 250)

## Combine cateory and numeric columns of test data.
test_data_combine = pd.concat([test_scaler, test_catcols_dummy], axis=1)

############################################### Decision Tree ##################################################################

## Import decision tree model.
from sklearn.tree import DecisionTreeRegressor

## Instantiate and fit a regression model.
dtr = DecisionTreeRegressor(max_depth=5,min_samples_leaf=10,min_samples_split=5,random_state=123)
dtr.fit(train_data_final,y_train)

DecisionTreeRegressor(ccp_alpha=0.0, criterion='mse', max_depth=5,
                      max_features=None, max_leaf_nodes=None,
                      min_impurity_decrease=0.0, min_impurity_split=None,
                      min_samples_leaf=10, min_samples_split=5,
                      min_weight_fraction_leaf=0.0, presort='deprecated',
                      random_state=123, splitter='best')

## Get the predictions on train and validation data.
pred_train = dtr.predict(train_data_final)
pred_test = dtr.predict(test_data_final)

## Get predictions for test data.
test_pred = dtr.predict(test_data_combine)

## Check dimensions of test data index column.
test_data.index.shape

(1459,)

## Check dimesnions of test predictions data.
test_pred.shape

(1459,)

## Prepare a dataframe with test data index,prediction values.
dataframe5 = pd.DataFrame({'Id' : test_data.index,
                          'SalePrice' : test_pred})

## Copy dataframe data into a CSV file.
dataframe5.to_csv('PredictionValues.csv',index=False)

## Import error metric libraries to measure RMSE.
from sklearn.metrics import mean_squared_error
from math import sqrt

## Display train and validation RMSE.
print("Train Error:",sqrt(mean_squared_error(y_train, pred_train)))
print("Test Error:",sqrt(mean_squared_error(y_test, pred_test)))

Train Error: 35145.50572611597
Test Error: 37672.08587542323

############################################## Random Forest ##################################################################

## Import random forest regressor model.
from sklearn.ensemble import RandomForestRegressor

## Instantiate a regressor model.
rc = RandomForestRegressor(n_estimators= 25, max_depth= 10)## ,min_samples_leaf = 2)## ,max_features='sqrt')

## Fit a model.
rc.fit(train_data_final,y_train)

RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',
                      max_depth=10, max_features='auto', max_leaf_nodes=None,
                      max_samples=None, min_impurity_decrease=0.0,
                      min_impurity_split=None, min_samples_leaf=1,
                      min_samples_split=2, min_weight_fraction_leaf=0.0,
                      n_estimators=25, n_jobs=None, oob_score=False,
                      random_state=None, verbose=0, warm_start=False)

## Get the predictions on train and validation data.
pred_train = rc.predict(train_data_final)
pred_test = rc.predict(test_data_final)

## Get predictions on test data.
test_pred = rc.predict(test_data_combine)

## Prepare a dataframe with test data index,prediction values.
dataframe6 = pd.DataFrame({'Id' : test_data.index,
                          'SalePrice' : test_pred})

## Copy dataframe data into a CSV file.
dataframe6.to_csv('PredictionValues.csv',index=False)

## Display RMSE values for train and validation data.
print("Train Error:",sqrt(mean_squared_error(y_train, pred_train)))
print("Test Error:",sqrt(mean_squared_error(y_test, pred_test)))

Train Error: 14189.808357485022
Test Error: 28007.627868664316

################################################### AdaBoost ##################################################################

## Import adaboost regressor model.
from sklearn.ensemble import AdaBoostRegressor

## Instantiate regressor model and fit it.
Adaboost_model = AdaBoostRegressor(n_estimators=50,learning_rate=1)
%time Adaboost_model.fit(train_data_final, y_train)

Wall time: 424 ms

AdaBoostRegressor(base_estimator=None, learning_rate=1.0, loss='linear',
                  n_estimators=50, random_state=None)

## Get the predictions on train and validation data.
pred_train = Adaboost_model.predict(train_data_final)
pred_test = Adaboost_model.predict(test_data_final)

## Get predictions on test data.
test_pred = Adaboost_model.predict(test_data_combine)

## Display RMSE value for train and validation data.
print("Train Error:",sqrt(mean_squared_error(y_train, pred_train)))
print("Test Error:",sqrt(mean_squared_error(y_test, pred_test)))

Train Error: 30520.585335079977
Test Error: 36586.41368072027

##################################################### GradientBoosting #########################################################

## Import Graident boot model library.
from sklearn.ensemble import GradientBoostingRegressor

## Innstantiate GBR and fit it.
gbm = GradientBoostingRegressor(n_estimators=50,learning_rate=0.8,random_state=474)
%time gbm.fit(X=train_data_final, y=y_train)

Wall time: 318 ms

GradientBoostingRegressor(alpha=0.9, ccp_alpha=0.0, criterion='friedman_mse',
                          init=None, learning_rate=0.8, loss='ls', max_depth=3,
                          max_features=None, max_leaf_nodes=None,
                          min_impurity_decrease=0.0, min_impurity_split=None,
                          min_samples_leaf=1, min_samples_split=2,
                          min_weight_fraction_leaf=0.0, n_estimators=50,
                          n_iter_no_change=None, presort='deprecated',
                          random_state=474, subsample=1.0, tol=0.0001,
                          validation_fraction=0.1, verbose=0, warm_start=False)

## Get the predictions on train and validation.
pred_train = gbm.predict(train_data_final)
pred_test = gbm.predict(test_data_final)

## Get predictions on test data.
test_pred = gbm.predict(test_data_combine)

## Dispay RMSE value for train and validation.
print("Train Error:",sqrt(mean_squared_error(y_train, pred_train)))
print("Test Error:",sqrt(mean_squared_error(y_test, pred_test)))

Train Error: 8570.43403879048
Test Error: 30745.40731980334

################################################## XGradient Boosting ##########################################################

## Import XGBoost model library.
import xgboost as xgb
from xgboost.sklearn import XGBRegressor

## Instantiate XGBR and fit it.
xgb_model=XGBRegressor(n_estimators=100,learning_rate=0.8)
%time xgb_model.fit(train_data_final,y_train,verbose=True)

C:\Users\nagar\Anaconda3\lib\site-packages\xgboost\core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version
  if getattr(data, 'base', None) is not None and \

[00:56:41] WARNING: C:/Jenkins/workspace/xgboost-win64_release_0.90/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
Wall time: 1.42 s

XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,
             colsample_bynode=1, colsample_bytree=1, gamma=0,
             importance_type='gain', learning_rate=0.8, max_delta_step=0,
             max_depth=3, min_child_weight=1, missing=None, n_estimators=100,
             n_jobs=1, nthread=None, objective='reg:linear', random_state=0,
             reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,
             silent=None, subsample=1, verbosity=1)

## Get the predictions on train and validation.
pred_train = xgb_model.predict(train_data_final)
pred_test = xgb_model.predict(test_data_final)

## Get predictions on test data.
test_pred = xgb_model.predict(test_data_combine)

## Prepare a dataframe with test inndex,preidction values.
dataframe7 = pd.DataFrame({'Id' : test_data.index,
                          'SalePrice' : test_pred})

## Copy dataframe data into a CSV file.
dataframe7.to_csv('PredictionValues.csv',index=False)

## Display scatter plot for actual target and prediction values.
plt.figure(figsize=(15,8))
plt.scatter(y_train,pred_train, c= 'brown')
plt.xlabel('Y Train')
plt.ylabel('Predicted Y')
plt.show()

## Get RMSE value for train and validation data.
print("Train Error:",sqrt(mean_squared_error(y_train, pred_train)))
print("Test Error:",sqrt(mean_squared_error(y_test, pred_test)))

Train Error: 4641.200474416745
Test Error: 32621.566438550162

#################################################### SVM #######################################################################

## Import SVR model library.
from sklearn.svm import SVR

## Instantiate SVR model.
svr_model = SVR()
svr_model

SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='scale',
    kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)

## Fit a model.
svr_model.fit(X = train_data_final, y = y_train)

SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='scale',
    kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)

## Get the predictions on train and validation.
pred_train = svr_model.predict(train_data_final)
pred_test = svr_model.predict(test_data_final)

## Get predictions on test data.
test_pred = svr_model.predict(test_data_combine)

## Get predictions on test data.
print("Train Error:",sqrt(mean_squared_error(y_train, pred_train)))
print("Test Error:",sqrt(mean_squared_error(y_test, pred_test)))

Train Error: 83315.89848130586
Test Error: 79142.24398988248

################################################### KNN #######################################################################

## Import KNN model library.
from sklearn.neighbors import KNeighborsRegressor

## Instantiate KNN model and fit it.
knn = KNeighborsRegressor(algorithm = 'brute', n_neighbors = 4,
                           metric = "euclidean")
knn.fit(train_data_final, y_train)

KNeighborsRegressor(algorithm='brute', leaf_size=30, metric='euclidean',
                    metric_params=None, n_jobs=None, n_neighbors=4, p=2,
                    weights='uniform')

## Get the predictions on train and validation.
pred_train = knn.predict(train_data_final)
pred_test = knn.predict(test_data_final)

## Get predictions on test data.
test_pred = knn.predict(test_data_combine)

## Display RMSE values fo train and validation.
print("Train Error:",sqrt(mean_squared_error(y_train, pred_train)))
print("Test Error:",sqrt(mean_squared_error(y_test, pred_test)))

Train Error: 31531.213116588428
Test Error: 37136.77105383857

############################################# Neural Network Linear Algoritham #################################################

## Import Sequential,Dense model libraries.
from keras.models import Sequential
from keras.layers import Dense

C:\Users\nagar\Anaconda3\lib\site-packages\h5py\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  from ._conv import register_converters as _register_converters
Using TensorFlow backend.

## Instantiate squential model.
model = Sequential()

## Add dense model.
model.add(Dense(1, input_dim=train_data_final.shape[1]))

## Add compiler to model.
model.compile(loss='mse', optimizer='rmsprop')

## Fit a model.
model.fit(train_data_final, y_train, epochs=150, batch_size=32)

Epoch 1/150
1022/1022 [==============================] - 0s 184us/step - loss: 38950132341.2290
Epoch 2/150
1022/1022 [==============================] - 0s 65us/step - loss: 38949634272.4384
Epoch 3/150
1022/1022 [==============================] - 0s 73us/step - loss: 38949164879.6556
Epoch 4/150
1022/1022 [==============================] - 0s 65us/step - loss: 38948694541.0254
Epoch 5/150
1022/1022 [==============================] - 0s 63us/step - loss: 38948221424.9706
Epoch 6/150
1022/1022 [==============================] - 0s 62us/step - loss: 38947748821.9178
Epoch 7/150
1022/1022 [==============================] - 0s 63us/step - loss: 38947278976.2505
Epoch 8/150
1022/1022 [==============================] - 0s 64us/step - loss: 38946805475.4442
Epoch 9/150
1022/1022 [==============================] - 0s 64us/step - loss: 38946334836.2270
Epoch 10/150
1022/1022 [==============================] - 0s 63us/step - loss: 38945865423.4051
Epoch 11/150
1022/1022 [==============================] - 0s 45us/step - loss: 38945391818.3953
Epoch 12/150
1022/1022 [==============================] - 0s 30us/step - loss: 38944927595.7104
Epoch 13/150
1022/1022 [==============================] - 0s 38us/step - loss: 38944455750.1370
Epoch 14/150
1022/1022 [==============================] - 0s 62us/step - loss: 38943984902.5127
Epoch 15/150
1022/1022 [==============================] - 0s 68us/step - loss: 38943514335.4364
Epoch 16/150
1022/1022 [==============================] - 0s 66us/step - loss: 38943043559.9530
Epoch 17/150
1022/1022 [==============================] - 0s 114us/step - loss: 38942572123.1781
Epoch 18/150
1022/1022 [==============================] - 0s 118us/step - loss: 38942101592.1722
Epoch 19/150
1022/1022 [==============================] - 0s 105us/step - loss: 38941631490.0039
Epoch 20/150
1022/1022 [==============================] - 0s 113us/step - loss: 38941163191.3581
Epoch 21/150
1022/1022 [==============================] - 0s 98us/step - loss: 38940687963.1781
Epoch 22/150
1022/1022 [==============================] - 0s 113us/step - loss: 38940215340.0861
Epoch 23/150
1022/1022 [==============================] - 0s 126us/step - loss: 38939746087.5773
Epoch 24/150
1022/1022 [==============================] - 0s 118us/step - loss: 38939272278.1683
Epoch 25/150
1022/1022 [==============================] - 0s 119us/step - loss: 38938800705.1272
Epoch 26/150
1022/1022 [==============================] - 0s 102us/step - loss: 38938326987.8982
Epoch 27/150
1022/1022 [==============================] - 0s 128us/step - loss: 38937856156.3053
Epoch 28/150
1022/1022 [==============================] - 0s 103us/step - loss: 38937385749.5421
Epoch 29/150
1022/1022 [==============================] - 0s 128us/step - loss: 38936916128.3131
Epoch 30/150
1022/1022 [==============================] - 0s 139us/step - loss: 38936443036.3053
Epoch 31/150
1022/1022 [==============================] - 0s 124us/step - loss: 38935966850.2544
Epoch 32/150
1022/1022 [==============================] - 0s 125us/step - loss: 38935498335.1859
Epoch 33/150
1022/1022 [==============================] - 0s 121us/step - loss: 38935027595.7730
Epoch 34/150
1022/1022 [==============================] - 0s 126us/step - loss: 38934554575.9061
Epoch 35/150
1022/1022 [==============================] - 0s 117us/step - loss: 38934078297.6751
Epoch 36/150
1022/1022 [==============================] - 0s 127us/step - loss: 38933606452.1018
Epoch 37/150
1022/1022 [==============================] - 0s 123us/step - loss: 38933137327.8434
Epoch 38/150
1022/1022 [==============================] - 0s 103us/step - loss: 38932665971.2250
Epoch 39/150
1022/1022 [==============================] - 0s 147us/step - loss: 38932197985.1898
Epoch 40/150
1022/1022 [==============================] - 0s 148us/step - loss: 38931723446.3562
Epoch 41/150
1022/1022 [==============================] - 0s 128us/step - loss: 38931247705.1742
Epoch 42/150
1022/1022 [==============================] - 0s 124us/step - loss: 38930781755.1155
Epoch 43/150
1022/1022 [==============================] - 0s 121us/step - loss: 38930308238.2779
Epoch 44/150
1022/1022 [==============================] - 0s 112us/step - loss: 38929839057.9100
Epoch 45/150
1022/1022 [==============================] - 0s 109us/step - loss: 38929368971.7730
Epoch 46/150
1022/1022 [==============================] - 0s 98us/step - loss: 38928897426.7867
Epoch 47/150
1022/1022 [==============================] - 0s 101us/step - loss: 38928424799.6869
Epoch 48/150
1022/1022 [==============================] - 0s 116us/step - loss: 38927952926.0587
Epoch 49/150
1022/1022 [==============================] - 0s 105us/step - loss: 38927480607.5616
Epoch 50/150
1022/1022 [==============================] - 0s 105us/step - loss: 38927012705.6908
Epoch 51/150
1022/1022 [==============================] - 0s 112us/step - loss: 38926542968.2348
Epoch 52/150
1022/1022 [==============================] - 0s 97us/step - loss: 38926072485.3229
Epoch 53/150
1022/1022 [==============================] - 0s 101us/step - loss: 38925599024.5949
Epoch 54/150
1022/1022 [==============================] - 0s 115us/step - loss: 38925129114.8023
Epoch 55/150
1022/1022 [==============================] - 0s 106us/step - loss: 38924656904.5166
Epoch 56/150
1022/1022 [==============================] - 0s 101us/step - loss: 38924186962.6614
Epoch 57/150
1022/1022 [==============================] - 0s 110us/step - loss: 38923716331.4599
Epoch 58/150
1022/1022 [==============================] - 0s 98us/step - loss: 38923243115.2094
Epoch 59/150
1022/1022 [==============================] - 0s 40us/step - loss: 38922773994.9589
Epoch 60/150
1022/1022 [==============================] - 0s 50us/step - loss: 38922302289.6595
Epoch 61/150
1022/1022 [==============================] - 0s 113us/step - loss: 38921833646.3405
Epoch 62/150
1022/1022 [==============================] - 0s 117us/step - loss: 38921363275.6477
Epoch 63/150
1022/1022 [==============================] - 0s 113us/step - loss: 38920891438.0900
Epoch 64/150
1022/1022 [==============================] - 0s 127us/step - loss: 38920420109.5264
Epoch 65/150
1022/1022 [==============================] - 0s 115us/step - loss: 38919952404.0391
Epoch 66/150
1022/1022 [==============================] - 0s 121us/step - loss: 38919485283.6947
Epoch 67/150
1022/1022 [==============================] - 0s 139us/step - loss: 38919016111.3425
Epoch 68/150
1022/1022 [==============================] - 0s 128us/step - loss: 38918539676.8063
Epoch 69/150
1022/1022 [==============================] - ETA: 0s - loss: 38476961319.384 - 0s 145us/step - loss: 38918067819.2094
Epoch 70/150
1022/1022 [==============================] - 0s 104us/step - loss: 38917594639.0294
Epoch 71/150
1022/1022 [==============================] - 0s 71us/step - loss: 38917127137.9413
Epoch 72/150
1022/1022 [==============================] - 0s 79us/step - loss: 38916659083.7730
Epoch 73/150
1022/1022 [==============================] - 0s 75us/step - loss: 38916187659.0215
Epoch 74/150
1022/1022 [==============================] - 0s 69us/step - loss: 38915717332.4149
Epoch 75/150
1022/1022 [==============================] - 0s 65us/step - loss: 38915245623.1076
Epoch 76/150
1022/1022 [==============================] - 0s 64us/step - loss: 38914777031.8904
Epoch 77/150
1022/1022 [==============================] - 0s 63us/step - loss: 38914304348.6810
Epoch 78/150
1022/1022 [==============================] - 0s 65us/step - loss: 38913836699.3033
Epoch 79/150
1022/1022 [==============================] - 0s 61us/step - loss: 38913364400.8454
Epoch 80/150
1022/1022 [==============================] - 0s 62us/step - loss: 38912893204.5401
Epoch 81/150
1022/1022 [==============================] - 0s 68us/step - loss: 38912422236.6810
Epoch 82/150
1022/1022 [==============================] - 0s 64us/step - loss: 38911953693.5577
Epoch 83/150
1022/1022 [==============================] - 0s 67us/step - loss: 38911478044.5558
Epoch 84/150
1022/1022 [==============================] - 0s 65us/step - loss: 38911008928.3131
Epoch 85/150
1022/1022 [==============================] - 0s 63us/step - loss: 38910541519.4051
Epoch 86/150
1022/1022 [==============================] - 0s 65us/step - loss: 38910071152.7202
Epoch 87/150
1022/1022 [==============================] - 0s 68us/step - loss: 38909592698.2387
Epoch 88/150
1022/1022 [==============================] - 0s 63us/step - loss: 38909126179.0685
Epoch 89/150
1022/1022 [==============================] - 0s 65us/step - loss: 38908655884.5245
Epoch 90/150
1022/1022 [==============================] - 0s 68us/step - loss: 38908182511.9687
Epoch 91/150
1022/1022 [==============================] - 0s 62us/step - loss: 38907713459.8513
Epoch 92/150
1022/1022 [==============================] - 0s 64us/step - loss: 38907244191.3112
Epoch 93/150
1022/1022 [==============================] - 0s 65us/step - loss: 38906776886.6067
Epoch 94/150
1022/1022 [==============================] - 0s 64us/step - loss: 38906303602.2231
Epoch 95/150
1022/1022 [==============================] - 0s 66us/step - loss: 38905834966.9198
Epoch 96/150
1022/1022 [==============================] - 0s 65us/step - loss: 38905361722.6145
Epoch 97/150
1022/1022 [==============================] - 0s 42us/step - loss: 38904891412.0391
Epoch 98/150
1022/1022 [==============================] - 0s 34us/step - loss: 38904422840.8611
Epoch 99/150
1022/1022 [==============================] - 0s 36us/step - loss: 38903954566.2622
Epoch 100/150
1022/1022 [==============================] - 0s 49us/step - loss: 38903485666.4423
Epoch 101/150
1022/1022 [==============================] - 0s 66us/step - loss: 38903014397.9961
Epoch 102/150
1022/1022 [==============================] - 0s 68us/step - loss: 38902547193.4873
Epoch 103/150
1022/1022 [==============================] - 0s 68us/step - loss: 38902074333.9335
Epoch 104/150
1022/1022 [==============================] - 0s 65us/step - loss: 38901601746.9119
Epoch 105/150
1022/1022 [==============================] - 0s 64us/step - loss: 38901134750.8102
Epoch 106/150
1022/1022 [==============================] - 0s 65us/step - loss: 38900659698.9746
Epoch 107/150
1022/1022 [==============================] - 0s 62us/step - loss: 38900187865.4247
Epoch 108/150
1022/1022 [==============================] - 0s 71us/step - loss: 38899720721.0333
Epoch 109/150
1022/1022 [==============================] - 0s 67us/step - loss: 38899249524.7280
Epoch 110/150
1022/1022 [==============================] - 0s 64us/step - loss: 38898777454.7162
Epoch 111/150
1022/1022 [==============================] - 0s 69us/step - loss: 38898310150.0117
Epoch 112/150
1022/1022 [==============================] - 0s 69us/step - loss: 38897836921.7378
Epoch 113/150
1022/1022 [==============================] - 0s 67us/step - loss: 38897367448.7984
Epoch 114/150
1022/1022 [==============================] - 0s 39us/step - loss: 38896895298.6301
Epoch 115/150
1022/1022 [==============================] - 0s 47us/step - loss: 38896427416.7984
Epoch 116/150
1022/1022 [==============================] - 0s 60us/step - loss: 38895957619.2250
Epoch 117/150
1022/1022 [==============================] - 0s 62us/step - loss: 38895486386.8493
Epoch 118/150
1022/1022 [==============================] - 0s 65us/step - loss: 38895017154.3796
Epoch 119/150
1022/1022 [==============================] - 0s 70us/step - loss: 38894545220.6341
Epoch 120/150
1022/1022 [==============================] - 0s 68us/step - loss: 38894076917.9804
Epoch 121/150
1022/1022 [==============================] - 0s 70us/step - loss: 38893607437.0254
Epoch 122/150
1022/1022 [==============================] - 0s 59us/step - loss: 38893137831.8278
Epoch 123/150
1022/1022 [==============================] - 0s 71us/step - loss: 38892668459.0841
Epoch 124/150
1022/1022 [==============================] - 0s 74us/step - loss: 38892196096.5010
Epoch 125/150
1022/1022 [==============================] - 0s 66us/step - loss: 38891727821.9022
Epoch 126/150
1022/1022 [==============================] - 0s 76us/step - loss: 38891260501.1663
Epoch 127/150
1022/1022 [==============================] - 0s 77us/step - loss: 38890792571.2407
Epoch 128/150
1022/1022 [==============================] - 0s 68us/step - loss: 38890321996.1487
Epoch 129/150
1022/1022 [==============================] - 0s 45us/step - loss: 38889853160.4540
Epoch 130/150
1022/1022 [==============================] - 0s 37us/step - loss: 38889383659.4599
Epoch 131/150
1022/1022 [==============================] - 0s 38us/step - loss: 38888916731.4912
Epoch 132/150
1022/1022 [==============================] - 0s 45us/step - loss: 38888446685.4325
Epoch 133/150
1022/1022 [==============================] - 0s 75us/step - loss: 38887980827.5538
Epoch 134/150
1022/1022 [==============================] - 0s 62us/step - loss: 38887511867.6164
Epoch 135/150
1022/1022 [==============================] - 0s 66us/step - loss: 38887042005.9178
Epoch 136/150
1022/1022 [==============================] - 0s 72us/step - loss: 38886571346.6614
Epoch 137/150
1022/1022 [==============================] - 0s 72us/step - loss: 38886098130.4110
Epoch 138/150
1022/1022 [==============================] - 0s 69us/step - loss: 38885629767.6399
Epoch 139/150
1022/1022 [==============================] - 0s 66us/step - loss: 38885158342.8885
Epoch 140/150
1022/1022 [==============================] - 0s 65us/step - loss: 38884692785.5969
Epoch 141/150
1022/1022 [==============================] - 0s 67us/step - loss: 38884222058.2074
Epoch 142/150
1022/1022 [==============================] - 0s 69us/step - loss: 38883749519.2798
Epoch 143/150
1022/1022 [==============================] - 0s 67us/step - loss: 38883275232.9393
Epoch 144/150
1022/1022 [==============================] - 0s 63us/step - loss: 38882804938.3953
Epoch 145/150
1022/1022 [==============================] - 0s 63us/step - loss: 38882336046.5910
Epoch 146/150
1022/1022 [==============================] - 0s 42us/step - loss: 38881863659.9609
Epoch 147/150
1022/1022 [==============================] - 0s 52us/step - loss: 38881392503.7339
Epoch 148/150
1022/1022 [==============================] - 0s 57us/step - loss: 38880921403.6164
Epoch 149/150
1022/1022 [==============================] - 0s 64us/step - loss: 38880450804.4775
Epoch 150/150
1022/1022 [==============================] - 0s 61us/step - loss: 38879982473.7691

<keras.callbacks.callbacks.History at 0x209042bbac8>

## Get the predictions on train and validation.
pred_train = model.predict(train_data_final)
pred_test = model.predict(test_data_final)

## Get predictions on test data.
test_pred = model.predict(test_data_combine)

## Display RMSE value for train and validation.
print("Train Error:",sqrt(mean_squared_error(y_train, pred_train)))
print("Test Error:",sqrt(mean_squared_error(y_test, pred_test)))

Train Error: 197179.45735965777
Test Error: 197930.06369128206

## Instantiate sequential model.
model1 = Sequential()

## Add 2 dense layes to model.
model1.add(Dense(8, input_dim=train_data_final.shape[1], activation='relu', kernel_initializer='uniform'))
model1.add(Dense(1, kernel_initializer='uniform'))

## Add compiler to model.
model.compile(loss='mse', optimizer='rmsprop')

## Fit a model.
model.fit(train_data_final, y_train, epochs=150, batch_size=32)

Epoch 1/150
1022/1022 [==============================] - 0s 101us/step - loss: 38879393034.5205
Epoch 2/150
1022/1022 [==============================] - 0s 34us/step - loss: 38878900817.1585
Epoch 3/150
1022/1022 [==============================] - 0s 34us/step - loss: 38878430346.2701
Epoch 4/150
1022/1022 [==============================] - 0s 32us/step - loss: 38877960348.3053
Epoch 5/150
1022/1022 [==============================] - 0s 35us/step - loss: 38877490029.7143
Epoch 6/150
1022/1022 [==============================] - 0s 32us/step - loss: 38877020520.7045
Epoch 7/150
1022/1022 [==============================] - 0s 40us/step - loss: 38876549120.0000
Epoch 8/150
1022/1022 [==============================] - ETA: 0s - loss: 53787824128.000 - 0s 34us/step - loss: 38876078805.4168
Epoch 9/150
1022/1022 [==============================] - 0s 32us/step - loss: 38875613256.1409
Epoch 10/150
1022/1022 [==============================] - 0s 35us/step - loss: 38875140568.9237
Epoch 11/150
1022/1022 [==============================] - 0s 37us/step - loss: 38874668246.4188
Epoch 12/150
1022/1022 [==============================] - 0s 37us/step - loss: 38874196156.3679
Epoch 13/150
1022/1022 [==============================] - 0s 44us/step - loss: 38873722928.0939
Epoch 14/150
1022/1022 [==============================] - 0s 39us/step - loss: 38873252593.4716
Epoch 15/150
1022/1022 [==============================] - 0s 46us/step - loss: 38872781441.2524
Epoch 16/150
1022/1022 [==============================] - 0s 45us/step - loss: 38872309158.8258
Epoch 17/150
1022/1022 [==============================] - 0s 45us/step - loss: 38871834415.5930
Epoch 18/150
1022/1022 [==============================] - 0s 54us/step - loss: 38871366469.6360
Epoch 19/150
1022/1022 [==============================] - 0s 61us/step - loss: 38870895597.9648
Epoch 20/150
1022/1022 [==============================] - 0s 64us/step - loss: 38870424926.6849
Epoch 21/150
1022/1022 [==============================] - 0s 62us/step - loss: 38869953277.4951
Epoch 22/150
1022/1022 [==============================] - 0s 63us/step - loss: 38869486557.9335
Epoch 23/150
1022/1022 [==============================] - 0s 67us/step - loss: 38869017337.4873
Epoch 24/150
1022/1022 [==============================] - 0s 32us/step - loss: 38868544582.1370
Epoch 25/150
1022/1022 [==============================] - 0s 31us/step - loss: 38868074804.6027
Epoch 26/150
1022/1022 [==============================] - 0s 39us/step - loss: 38867604017.0959
Epoch 27/150
1022/1022 [==============================] - 0s 45us/step - loss: 38867137850.6145
Epoch 28/150
1022/1022 [==============================] - 0s 62us/step - loss: 38866666229.4795
Epoch 29/150
1022/1022 [==============================] - 0s 63us/step - loss: 38866194840.7984
Epoch 30/150
1022/1022 [==============================] - 0s 65us/step - loss: 38865725179.4912
Epoch 31/150
1022/1022 [==============================] - 0s 61us/step - loss: 38865252632.5479
Epoch 32/150
1022/1022 [==============================] - 0s 63us/step - loss: 38864782790.8885
Epoch 33/150
1022/1022 [==============================] - 0s 61us/step - loss: 38864315578.3640
Epoch 34/150
1022/1022 [==============================] - 0s 61us/step - loss: 38863844826.9276
Epoch 35/150
1022/1022 [==============================] - 0s 59us/step - loss: 38863375366.0117
Epoch 36/150
1022/1022 [==============================] - 0s 58us/step - loss: 38862903973.3229
Epoch 37/150
1022/1022 [==============================] - 0s 50us/step - loss: 38862436492.2740
Epoch 38/150
1022/1022 [==============================] - 0s 40us/step - loss: 38861967436.1487
Epoch 39/150
1022/1022 [==============================] - 0s 49us/step - loss: 38861495686.7632
Epoch 40/150
1022/1022 [==============================] - 0s 59us/step - loss: 38861025031.5147
Epoch 41/150
1022/1022 [==============================] - 0s 64us/step - loss: 38860555013.5108
Epoch 42/150
1022/1022 [==============================] - 0s 49us/step - loss: 38860082999.6086
Epoch 43/150
1022/1022 [==============================] - 0s 33us/step - loss: 38859612272.2192
Epoch 44/150
1022/1022 [==============================] - 0s 36us/step - loss: 38859143396.4462
Epoch 45/150
1022/1022 [==============================] - 0s 38us/step - loss: 38858674424.4853
Epoch 46/150
1022/1022 [==============================] - 0s 54us/step - loss: 38858204354.3796
Epoch 47/150
1022/1022 [==============================] - 0s 67us/step - loss: 38857734532.7593
Epoch 48/150
1022/1022 [==============================] - 0s 67us/step - loss: 38857264013.7769
Epoch 49/150
1022/1022 [==============================] - 0s 43us/step - loss: 38856790400.7515
Epoch 50/150
1022/1022 [==============================] - 0s 40us/step - loss: 38856322438.7632
Epoch 51/150
1022/1022 [==============================] - 0s 64us/step - loss: 38855850036.1018
Epoch 52/150
1022/1022 [==============================] - 0s 84us/step - loss: 38855380947.9139
Epoch 53/150
1022/1022 [==============================] - 0s 89us/step - loss: 38854905615.5303
Epoch 54/150
1022/1022 [==============================] - 0s 84us/step - loss: 38854435296.9393
Epoch 55/150
1022/1022 [==============================] - 0s 82us/step - loss: 38853971006.1213
Epoch 56/150
1022/1022 [==============================] - 0s 86us/step - loss: 38853499332.8845
Epoch 57/150
1022/1022 [==============================] - 0s 80us/step - loss: 38853030064.3444
Epoch 58/150
1022/1022 [==============================] - 0s 50us/step - loss: 38852561645.4638
Epoch 59/150
1022/1022 [==============================] - 0s 30us/step - loss: 38852092801.7534
Epoch 60/150
1022/1022 [==============================] - 0s 42us/step - loss: 38851620162.6301
Epoch 61/150
1022/1022 [==============================] - 0s 62us/step - loss: 38851149595.5538
Epoch 62/150
1022/1022 [==============================] - 0s 62us/step - loss: 38850680667.6791
Epoch 63/150
1022/1022 [==============================] - 0s 65us/step - loss: 38850207335.2016
Epoch 64/150
1022/1022 [==============================] - 0s 69us/step - loss: 38849736367.3425
Epoch 65/150
1022/1022 [==============================] - 0s 64us/step - loss: 38849262698.2074
Epoch 66/150
1022/1022 [==============================] - 0s 66us/step - loss: 38848793261.3386
Epoch 67/150
1022/1022 [==============================] - 0s 61us/step - loss: 38848324473.7378
Epoch 68/150
1022/1022 [==============================] - 0s 35us/step - loss: 38847850956.9002
Epoch 69/150
1022/1022 [==============================] - 0s 73us/step - loss: 38847385896.5793
Epoch 70/150
1022/1022 [==============================] - 0s 75us/step - loss: 38846918183.0763
Epoch 71/150
1022/1022 [==============================] - 0s 77us/step - loss: 38846447355.4912
Epoch 72/150
1022/1022 [==============================] - 0s 81us/step - loss: 38845979124.9785
Epoch 73/150
1022/1022 [==============================] - 0s 76us/step - loss: 38845504710.3875
Epoch 74/150
1022/1022 [==============================] - 0s 60us/step - loss: 38845038696.2035
Epoch 75/150
1022/1022 [==============================] - 0s 32us/step - loss: 38844569908.6027
Epoch 76/150
1022/1022 [==============================] - 0s 50us/step - loss: 38844095790.5910
Epoch 77/150
1022/1022 [==============================] - 0s 62us/step - loss: 38843621504.2505
Epoch 78/150
1022/1022 [==============================] - 0s 62us/step - loss: 38843153365.9178
Epoch 79/150
1022/1022 [==============================] - 0s 69us/step - loss: 38842683680.5636
Epoch 80/150
1022/1022 [==============================] - 0s 67us/step - loss: 38842210243.8826
Epoch 81/150
1022/1022 [==============================] - 0s 71us/step - loss: 38841743267.8200
Epoch 82/150
1022/1022 [==============================] - 0s 34us/step - loss: 38841270031.5303
Epoch 83/150
1022/1022 [==============================] - 0s 51us/step - loss: 38840799360.2505
Epoch 84/150
1022/1022 [==============================] - 0s 63us/step - loss: 38840329474.5049
Epoch 85/150
1022/1022 [==============================] - 0s 66us/step - loss: 38839858915.4442
Epoch 86/150
1022/1022 [==============================] - 0s 69us/step - loss: 38839388532.7280
Epoch 87/150
1022/1022 [==============================] - 0s 63us/step - loss: 38838917364.4775
Epoch 88/150
1022/1022 [==============================] - 0s 69us/step - loss: 38838450797.2133
Epoch 89/150
1022/1022 [==============================] - 0s 66us/step - loss: 38837976238.3405
Epoch 90/150
1022/1022 [==============================] - 0s 66us/step - loss: 38837507106.0665
Epoch 91/150
1022/1022 [==============================] - 0s 48us/step - loss: 38837029918.0587
Epoch 92/150
1022/1022 [==============================] - 0s 31us/step - loss: 38836562589.3072
Epoch 93/150
1022/1022 [==============================] - 0s 34us/step - loss: 38836093553.2211
Epoch 94/150
1022/1022 [==============================] - 0s 39us/step - loss: 38835620733.7456
Epoch 95/150
1022/1022 [==============================] - 0s 56us/step - loss: 38835155320.7358
Epoch 96/150
1022/1022 [==============================] - 0s 67us/step - loss: 38834682421.1037
Epoch 97/150
1022/1022 [==============================] - 0s 66us/step - loss: 38834213946.1135
Epoch 98/150
1022/1022 [==============================] - 0s 78us/step - loss: 38833745302.7945
Epoch 99/150
1022/1022 [==============================] - 0s 67us/step - loss: 38833277949.9961
Epoch 100/150
1022/1022 [==============================] - 0s 66us/step - loss: 38832806332.8689
Epoch 101/150
1022/1022 [==============================] - 0s 53us/step - loss: 38832337501.1820
Epoch 102/150
1022/1022 [==============================] - 0s 40us/step - loss: 38831865956.1957
Epoch 103/150
1022/1022 [==============================] - 0s 58us/step - loss: 38831397685.6047
Epoch 104/150
1022/1022 [==============================] - 0s 78us/step - loss: 38830925483.3346
Epoch 105/150
1022/1022 [==============================] - 0s 87us/step - loss: 38830453982.4344
Epoch 106/150
1022/1022 [==============================] - 0s 86us/step - loss: 38829983651.8200
Epoch 107/150
1022/1022 [==============================] - 0s 81us/step - loss: 38829512884.3523
Epoch 108/150
1022/1022 [==============================] - 0s 31us/step - loss: 38829046777.9883
Epoch 109/150
1022/1022 [==============================] - 0s 33us/step - loss: 38828575269.0724
Epoch 110/150
1022/1022 [==============================] - 0s 35us/step - loss: 38828102978.6301
Epoch 111/150
1022/1022 [==============================] - 0s 51us/step - loss: 38827629694.2466
Epoch 112/150
1022/1022 [==============================] - 0s 72us/step - loss: 38827161531.8669
Epoch 113/150
1022/1022 [==============================] - 0s 70us/step - loss: 38826688984.9237
Epoch 114/150
1022/1022 [==============================] - 0s 69us/step - loss: 38826218225.4716
Epoch 115/150
1022/1022 [==============================] - 0s 65us/step - loss: 38825744219.6791
Epoch 116/150
1022/1022 [==============================] - 0s 71us/step - loss: 38825274935.1076
Epoch 117/150
1022/1022 [==============================] - 0s 66us/step - loss: 38824804792.8611
Epoch 118/150
1022/1022 [==============================] - 0s 79us/step - loss: 38824336971.1468
Epoch 119/150
1022/1022 [==============================] - 0s 82us/step - loss: 38823862913.2524
Epoch 120/150
1022/1022 [==============================] - 0s 80us/step - loss: 38823389833.2681
Epoch 121/150
1022/1022 [==============================] - 0s 77us/step - loss: 38822922268.0548
Epoch 122/150
1022/1022 [==============================] - 0s 72us/step - loss: 38822453961.3933
Epoch 123/150
1022/1022 [==============================] - 0s 73us/step - loss: 38821983402.3327
Epoch 124/150
1022/1022 [==============================] - 0s 75us/step - loss: 38821516690.7867
Epoch 125/150
1022/1022 [==============================] - 0s 74us/step - loss: 38821048784.9080
Epoch 126/150
1022/1022 [==============================] - 0s 70us/step - loss: 38820574662.8885
Epoch 127/150
1022/1022 [==============================] - 0s 79us/step - loss: 38820103218.0978
Epoch 128/150
1022/1022 [==============================] - 0s 71us/step - loss: 38819632410.5519
Epoch 129/150
1022/1022 [==============================] - 0s 69us/step - loss: 38819166131.8513
Epoch 130/150
1022/1022 [==============================] - 0s 67us/step - loss: 38818695709.0568
Epoch 131/150
1022/1022 [==============================] - 0s 59us/step - loss: 38818223089.9726
Epoch 132/150
1022/1022 [==============================] - 0s 83us/step - loss: 38817755961.6125
Epoch 133/150
1022/1022 [==============================] - 0s 79us/step - loss: 38817283342.5284
Epoch 134/150
1022/1022 [==============================] - 0s 81us/step - loss: 38816815845.4481
Epoch 135/150
1022/1022 [==============================] - 0s 79us/step - loss: 38816346368.5010
Epoch 136/150
1022/1022 [==============================] - 0s 80us/step - loss: 38815873096.1409
Epoch 137/150
1022/1022 [==============================] - 0s 37us/step - loss: 38815397924.0704
Epoch 138/150
1022/1022 [==============================] - 0s 32us/step - loss: 38814930835.7886
Epoch 139/150
1022/1022 [==============================] - 0s 38us/step - loss: 38814460196.5714
Epoch 140/150
1022/1022 [==============================] - 0s 37us/step - loss: 38813990374.9511
Epoch 141/150
1022/1022 [==============================] - 0s 48us/step - loss: 38813525507.0059
Epoch 142/150
1022/1022 [==============================] - 0s 60us/step - loss: 38813056795.5538
Epoch 143/150
1022/1022 [==============================] - 0s 64us/step - loss: 38812583567.2798
Epoch 144/150
1022/1022 [==============================] - 0s 68us/step - loss: 38812111681.6282
Epoch 145/150
1022/1022 [==============================] - 0s 68us/step - loss: 38811647018.0822
Epoch 146/150
1022/1022 [==============================] - 0s 66us/step - loss: 38811179933.8082
Epoch 147/150
1022/1022 [==============================] - 0s 63us/step - loss: 38810707531.1468
Epoch 148/150
1022/1022 [==============================] - 0s 88us/step - loss: 38810237477.0724
Epoch 149/150
1022/1022 [==============================] - 0s 87us/step - loss: 38809761735.8904
Epoch 150/150
1022/1022 [==============================] - 0s 90us/step - loss: 38809294607.5303

<keras.callbacks.callbacks.History at 0x20913acf668>

## Get the predictions on train and validation.
pred_train = model1.predict(train_data_final)
pred_test = model1.predict(test_data_final)

## Get predictions on test data.
test_pred = model1.predict(test_data_combine)

## Display RMSE value for train and vallidation data.
print("Train Error:",sqrt(mean_squared_error(y_train, pred_train)))
print("Test Error:",sqrt(mean_squared_error(y_test, pred_test)))

Train Error: 197358.3163285996
Test Error: 198108.21506572075

############################################## PCA ###########################################################################

## Import PCA model library.
from sklearn.decomposition import PCA

## Instantiate PCA model and fit it.
pca = PCA(n_components=2)
principalComponents = pca.fit_transform(X_train_scaler)

## Get dimensions of train data.
train_data_final.shape

(1022, 277)

## Get dimensions of pca coponnents.
principalComponents.shape

(1022, 2)

## Display  principal components.
print(principalComponents)

[[-0.8954133  -2.18631755]
 [-2.66539918  0.43482128]
 [-2.09868063 -0.79797737]
 ...
 [ 2.48650134  0.28487654]
 [-1.46838751 -2.10796887]
 [-1.58717077 -0.00724692]]

## Prepare a dataframe with principal component.
principalDf = pd.DataFrame(data = principalComponents
             , columns = ['principal component 1', 'principal component 2'])

## Get first  5 records of principal component.
principalDf.head()

## Get  varience ratio.
pca.explained_variance_ratio_

array([0.21192618, 0.10387824])

## Get components.
pca.components_

array([[ 0.21954716,  0.13919059,  0.25063762,  0.21109311,  0.22612689,
         0.18639512,  0.00789492,  0.1209049 ,  0.31321585,  0.31675222,
         0.11520559, -0.01314929,  0.32642755,  0.12633061,  0.26510875,
         0.22884204,  0.31064052,  0.3227923 ,  0.139032  ,  0.16942426,
        -0.08594562,  0.02502759,  0.0376397 ,  0.09271425,  0.0159143 ,
         0.01729143, -0.01169429],
       [ 0.12553739,  0.09021614, -0.32818343, -0.20498363, -0.02720514,
        -0.13671027,  0.0109314 ,  0.03405799, -0.10444001, -0.03336502,
         0.39664523,  0.18821754,  0.31354295,  0.41962082,  0.37018061,
        -0.28723628, -0.11852569, -0.11385579, -0.05821965,  0.03671219,
         0.19723035, -0.02522341,  0.06950059,  0.14015004,  0.09141491,
         0.03752889, -0.0379974 ]])

## Get features value.
pca.n_features_

27

## Get the predictions on train and validation data.
train_pca = pca.transform(X_train_scaler)
test_pca = pca.transform(X_test_scaler)

## Display train prediction vallues.
train_pca

array([[-0.89541298, -2.18634211],
       [-2.66540047,  0.435051  ],
       [-2.09868065, -0.79802406],
       ...,
       [ 2.48650239,  0.28477943],
       [-1.46838756, -2.10795479],
       [-1.58717065, -0.00720368]])

## Display validation prediction vallues.
test_pca

array([[ 1.15631213e+00,  5.95098354e-01],
       [-4.51084517e-01,  3.07914898e+00],
       [ 1.70842552e-01, -3.86034495e-01],
       [ 2.13042556e+00,  3.09347366e-01],
       [-1.92999453e+00,  9.29849697e-01],
       [ 3.50776552e+00, -1.06220920e+00],
       [ 3.46897325e+00,  1.70532045e+00],
       [-2.66206957e+00, -4.68360625e-01],
       [-4.79949640e-01, -1.35964165e-02],
       [-1.51951014e+00, -6.16007414e-01],
       [ 5.75927321e-01, -1.68190468e+00],
       [ 1.21260292e+00,  5.76601800e-01],
       [-8.75171999e-01, -2.57685380e+00],
       [-1.87794288e+00, -1.71568191e-01],
       [ 2.61642739e+00,  2.37553244e+00],
       [ 4.52825404e-01, -7.90378504e-01],
       [ 2.44573639e-02,  1.06509568e+00],
       [ 4.23283242e+00, -7.68756629e-01],
       [ 1.06689672e+00, -2.00248321e-01],
       [ 5.81676000e-01,  1.59294388e+00],
       [-8.95318581e-01, -5.63821750e-01],
       [ 2.50095525e-01,  1.83548069e+00],
       [-2.81000784e+00, -4.06339451e-01],
       [-1.96392923e+00,  1.30764016e+00],
       [ 9.01045732e-01, -3.34261117e-01],
       [ 1.08580841e+00, -1.18578419e+00],
       [ 1.69796945e+00, -1.31013720e+00],
       [ 6.85113862e-01,  1.64829298e-01],
       [-1.64203118e+00, -5.60084235e-01],
       [-1.34744183e-01, -8.43595522e-01],
       [-9.06787824e-01, -3.06272811e-01],
       [ 1.53601651e+00, -1.14929645e+00],
       [-7.87317929e-01, -3.41414787e-01],
       [ 1.60495797e+00, -1.79014071e+00],
       [ 4.15783199e+00, -2.97299661e+00],
       [ 1.59888023e+00, -1.53721895e+00],
       [ 1.00808006e-01, -1.30452524e+00],
       [ 4.44819933e+00,  7.30975737e+00],
       [ 6.28605006e-01, -5.52121223e-01],
       [-3.24248763e+00,  1.56586965e+00],
       [-1.02348712e+00, -1.87929718e+00],
       [ 1.81907364e+00, -2.16475571e+00],
       [ 1.42281589e+00,  6.67058593e-01],
       [ 1.09169381e+00, -2.24876358e+00],
       [ 1.73841080e+00, -7.87096855e-01],
       [-2.85085831e+00, -7.82668820e-01],
       [ 4.02556212e+00, -1.22034661e+00],
       [ 1.10099290e+00,  1.82921313e-01],
       [ 1.94271868e+00,  3.46748783e-02],
       [-2.88007069e+00,  1.87603014e+00],
       [ 9.72037371e-01,  1.55665603e+00],
       [ 5.12336737e-01, -3.36790312e+00],
       [-1.01291833e+00, -6.59773884e-01],
       [ 1.57729074e-01, -4.19008707e-01],
       [-3.19854879e+00,  1.48457420e+00],
       [-8.77609394e-01,  8.79886358e-01],
       [-2.24898705e+00, -9.51207750e-01],
       [ 6.65703150e-01, -9.67564626e-01],
       [ 2.37442540e+00, -1.30639424e+00],
       [ 4.16994292e+00, -1.17350171e+00],
       [-1.51862276e+00, -1.42411530e+00],
       [ 3.64927708e+00,  1.32611174e+00],
       [-1.77630674e+00,  4.09452419e+00],
       [-3.56729582e-01, -1.02624538e+00],
       [ 9.34371362e-01,  2.03568283e-01],
       [-2.61016086e+00,  3.30155479e+00],
       [-3.47932428e+00,  1.32291856e+00],
       [ 3.04321680e+00, -1.44706253e+00],
       [ 2.13492487e+00, -1.12013206e+00],
       [-6.07508391e-01, -5.44293038e-01],
       [-1.06487187e+00, -1.85578362e+00],
       [ 1.48250602e+00,  1.55901104e+00],
       [-8.77895129e-01, -1.84138137e+00],
       [ 3.24162650e+00, -1.30819064e+00],
       [-5.78243624e-01,  2.48765908e+00],
       [-2.37609723e+00, -2.81152823e-01],
       [ 2.47215018e-01, -3.34708978e-01],
       [-1.20025313e+00, -1.55412327e+00],
       [ 1.27032515e+00,  9.64662650e-01],
       [-2.09795618e+00, -3.11555082e+00],
       [ 3.48267560e+00, -4.77482269e-01],
       [ 4.54415875e+00, -1.99007660e+00],
       [-4.52901423e+00, -2.99780505e-01],
       [-1.98888380e+00,  3.71989732e-02],
       [ 3.40050573e+00, -1.16422339e+00],
       [-1.13748454e+00, -2.12438847e-01],
       [-3.99488094e+00,  2.62272427e-02],
       [-2.58950513e+00,  4.43930219e-01],
       [-3.43592026e+00, -2.14991395e-02],
       [ 3.65538290e+00, -9.50462573e-01],
       [-1.75605494e+00,  7.93575681e-01],
       [ 5.99155256e-01,  1.47865977e-01],
       [ 2.20900346e-01,  1.52401824e-01],
       [-1.13667723e+00,  1.84341028e+00],
       [-7.47092238e-02, -1.70063443e+00],
       [-2.80871136e+00,  1.81525865e+00],
       [-5.47329247e-01,  5.02436921e+00],
       [-2.74146997e+00, -7.39738863e-01],
       [ 1.88755107e+00,  8.35725082e-01],
       [-2.40078350e+00, -8.18529469e-01],
       [-2.43017051e+00,  2.25149528e+00],
       [ 6.50875179e+00,  2.01779985e+00],
       [ 4.87777177e+00,  1.66832880e+00],
       [-3.20884589e-01, -7.67974751e-01],
       [ 1.01775823e+00, -4.14753660e-01],
       [-2.46413973e+00,  2.78457874e+00],
       [-2.44507526e+00, -8.29428594e-01],
       [ 6.02141520e-01, -1.28073036e+00],
       [-1.32772651e-01, -7.45960483e-01],
       [ 3.71228678e+00, -1.68469832e+00],
       [ 3.86735400e+00,  5.84810269e-01],
       [ 3.63273998e+00,  2.69770492e+00],
       [-3.06440692e+00,  1.18430859e+00],
       [ 2.99269328e+00,  4.36819191e-03],
       [ 1.00316581e+00, -2.06797775e+00],
       [-2.65633465e+00, -2.74943403e+00],
       [ 2.75284142e-01, -1.21256911e+00],
       [ 5.34729684e+00,  5.34867220e-01],
       [-2.69857067e+00, -3.05516168e-01],
       [-1.19555198e+00, -1.56606747e+00],
       [-5.46867551e-02,  7.10952688e-01],
       [ 1.13374286e+00, -1.09704821e+00],
       [ 9.31381639e-01, -2.37800815e+00],
       [-1.77274064e+00, -1.20908771e+00],
       [ 1.07545601e+00, -3.30169745e+00],
       [ 3.18039178e+00, -1.72598486e+00],
       [-3.20140029e+00, -3.66761268e-01],
       [-2.45265046e+00, -1.09522143e+00],
       [-6.09623831e-01, -2.16774787e+00],
       [ 4.19629362e+00,  2.88844373e-01],
       [-8.13024776e-01,  6.74135979e-01],
       [ 3.21792102e+00, -2.17157363e+00],
       [ 2.75397374e+00,  1.55373289e+00],
       [ 3.99965190e-01,  6.03441390e-03],
       [ 2.28821382e-01,  2.13210497e+00],
       [ 1.10983999e+00,  2.56796529e-01],
       [ 3.56531327e+00,  3.79703887e-01],
       [-3.59602464e+00, -3.22186393e-01],
       [-3.30368864e-01, -1.18964938e+00],
       [-2.51885304e+00, -7.99446935e-02],
       [-2.18952323e+00,  1.26858541e+00],
       [ 2.89395053e+00,  8.59902897e-02],
       [ 2.96455241e+00,  8.88974117e-01],
       [-8.05398308e-01, -8.89930682e-01],
       [ 1.89444828e+00, -1.01423695e+00],
       [ 3.89898789e+00,  2.74104382e+00],
       [ 3.40115446e+00, -1.46623603e+00],
       [-3.27639643e-01,  2.19188614e+00],
       [ 9.50302179e-01, -4.28467937e-01],
       [-1.42559930e+00,  1.16832970e+00],
       [-7.01947444e-02, -5.02355280e-01],
       [-1.65565543e+00, -4.30587568e-01],
       [ 5.73343019e+00,  1.50101437e+00],
       [-3.76911509e-01, -7.69376329e-01],
       [ 1.99402868e+00, -1.65433182e+00],
       [-4.71912528e-01, -2.69977341e+00],
       [ 2.37816740e+00, -9.53778230e-01],
       [-1.96728348e+00,  5.49805639e-01],
       [-1.23288318e+00, -6.49184944e-01],
       [-1.15765620e+00, -1.98417634e+00],
       [ 8.24692059e+00,  2.54665248e+00],
       [ 1.61960508e+00,  5.77531649e-01],
       [ 2.76451774e+00,  1.76829382e+00],
       [-1.30906083e+00,  1.29170342e+00],
       [ 3.13350683e+00, -1.94894326e+00],
       [-2.78432018e+00,  3.03008856e+00],
       [-8.45143471e-01,  1.77948228e-01],
       [-1.11518258e+00,  8.91119241e-02],
       [ 6.62175158e-01, -1.40530716e+00],
       [-2.02549035e+00, -2.35229402e-01],
       [-1.89636353e+00, -3.83971390e-01],
       [-1.04108906e+00,  1.55229552e+00],
       [ 5.92004667e-01,  2.18734841e+00],
       [ 1.79448034e+00, -3.04515717e+00],
       [-7.16705410e-01, -7.84753486e-01],
       [ 3.59264995e+00, -4.73598784e-02],
       [ 5.96369315e-01, -1.49682352e+00],
       [-2.47422575e+00, -1.20878025e-01],
       [ 8.28920506e-01, -2.25971769e+00],
       [ 3.26595400e+00, -1.37114206e+00],
       [-3.61628499e-01, -1.25934606e+00],
       [-9.57890683e-01, -2.60073403e+00],
       [-1.91596391e+00, -9.13735444e-01],
       [ 2.69398113e-01, -1.20988639e+00],
       [-2.53892300e-01,  1.89680009e-01],
       [-1.78548711e+00, -9.21353820e-01],
       [-1.78798239e+00,  2.72383778e+00],
       [ 5.51674606e-01, -2.15278069e+00],
       [ 2.54655732e-01, -6.17384611e-01],
       [-4.51148924e-01, -1.08104641e+00],
       [-2.21177015e+00,  7.22333887e-01],
       [-1.82278242e+00,  2.05847942e-02],
       [ 3.07282200e-01, -3.31547650e-01],
       [ 2.48129284e+00, -1.63706683e+00],
       [-1.57632369e+00,  9.79735574e-01],
       [ 3.69841974e+00,  1.51004555e+00],
       [ 1.58917532e-01, -1.82897503e+00],
       [-3.18222172e+00,  2.86739755e-01],
       [-5.16876840e-01,  2.25759619e-01],
       [-3.13378943e+00, -6.06768803e-01],
       [ 5.24577500e-01, -4.74949353e-02],
       [ 3.70357779e+00, -1.69558235e+00],
       [-1.58792535e-01, -2.45263684e-01],
       [ 4.36988029e+00,  8.60982568e-01],
       [ 1.11561923e+00,  1.52290917e+00],
       [-1.67257571e+00, -1.50422926e-01],
       [-3.38315000e+00, -1.09305734e+00],
       [ 3.85984599e+00,  2.33996049e+00],
       [-2.49747556e-01, -9.80131565e-01],
       [ 4.98126385e-01, -5.98488159e-01],
       [-1.81495567e+00,  2.30506695e+00],
       [ 4.61835667e+00, -1.13549778e+00],
       [-2.28001942e+00,  2.12275065e-01],
       [-1.37065892e+00,  1.16562343e-02],
       [-9.88149685e-01, -9.76676474e-01],
       [-4.07954919e-01, -2.65545210e+00],
       [ 6.03911808e-01, -1.02691513e-01],
       [-2.14000392e+00,  1.46799100e+00],
       [-1.85682269e+00, -8.08676803e-01],
       [-3.39123375e+00,  4.52357203e-01],
       [ 3.52922904e-01, -3.32939726e-01],
       [-4.33065915e+00,  9.91902488e-01],
       [ 2.45318213e+00,  8.56786518e-01],
       [-2.23650189e+00,  1.55487422e+00],
       [-2.10212555e+00,  2.74391594e-01],
       [-2.68177486e+00,  3.42908161e+00],
       [-6.50028179e-02, -2.73161535e-01],
       [-3.08916399e+00, -8.70875685e-01],
       [-5.91746812e-02, -2.76186359e-01],
       [-8.60957888e-01,  6.24809298e-01],
       [-2.21082142e+00,  2.72732530e+00],
       [-1.05770430e+00, -3.66215415e+00],
       [ 2.44139362e-01,  2.01278002e+00],
       [-2.43978829e+00,  3.07290990e-01],
       [-2.10650873e+00, -6.92506424e-01],
       [-3.52923973e-01,  1.65786901e-01],
       [-1.86436193e-01,  4.79214969e-01],
       [-2.02336840e+00, -1.40074254e-01],
       [ 4.63034023e-01, -6.64755824e-02],
       [-2.88942875e+00, -2.73719486e-02],
       [ 2.23561257e+00, -1.15629338e-01],
       [ 1.81718838e+00, -6.43812179e-02],
       [ 1.52547802e+00, -1.15088479e+00],
       [ 1.43279825e+00,  1.43535599e+00],
       [-1.55958622e+00, -2.79569080e-01],
       [ 2.44724811e+00,  6.25571158e-01],
       [-3.36875530e-01,  3.67176523e+00],
       [ 3.08693232e+00, -1.78589058e+00],
       [-2.22417657e+00, -1.64065183e+00],
       [ 2.32781047e+00, -3.53702853e-01],
       [ 3.07785389e+00, -1.50385306e+00],
       [-2.76609605e+00,  8.66910376e-01],
       [ 6.52566474e-01, -3.28009746e-01],
       [-3.00131293e+00,  1.45839820e+00],
       [ 3.41259025e-02, -1.28182135e-02],
       [ 1.18347353e+00,  4.16947196e-01],
       [ 2.43177958e+00, -2.28031866e+00],
       [ 9.58281856e-01, -2.00809415e+00],
       [-9.04530014e-01, -7.49861880e-01],
       [-1.81317435e+00, -3.58522055e-01],
       [ 1.50239647e+00, -2.57364583e-01],
       [ 6.98398027e-01, -2.08482531e+00],
       [ 2.43748533e-01, -1.38319311e+00],
       [ 7.35087282e-01,  1.85494518e+00],
       [ 3.15371584e-01, -8.10563902e-02],
       [-5.47791780e-01,  1.85838734e+00],
       [ 1.86755031e+00,  1.26761837e+00],
       [ 3.95647361e-01, -2.79847886e-01],
       [-4.35414283e+00,  6.25103249e-01],
       [-2.34452713e+00, -1.52555336e+00],
       [-2.06957052e+00,  2.40354676e+00],
       [-3.47317949e+00,  4.70451460e-02],
       [ 2.11344623e+00, -1.02177579e+00],
       [-3.98665127e+00, -1.25659392e+00],
       [ 2.94674612e+00, -1.74114614e-01],
       [ 2.17666614e-01,  3.28555479e-01],
       [ 1.38330917e-01, -2.08089075e+00],
       [ 1.79532948e+00,  1.54232738e+00],
       [-1.78835779e+00,  1.20101597e+00],
       [-2.32887859e+00, -1.55207317e-01],
       [-2.38841819e+00,  1.41330139e+00],
       [-6.34711976e-01, -4.79627156e-01],
       [-2.58020361e+00,  6.25139120e-01],
       [-9.37717720e-01, -4.62758415e-02],
       [-9.86985845e-02, -1.52024273e+00],
       [-2.42590389e+00,  1.27853060e+00],
       [ 5.55036983e-01,  5.72680917e-03],
       [-2.54401993e+00,  2.20388894e+00],
       [-2.51638707e-01,  1.63984745e+00],
       [-1.05303240e+00, -1.52146351e-01],
       [ 1.38250390e+00,  2.52129640e+00],
       [-3.46427081e+00,  9.20146922e-01],
       [ 3.71939571e+00,  1.80504033e+00],
       [ 5.99819545e-01, -1.85698585e-01],
       [ 4.62019370e+00,  2.48683196e+00],
       [-3.64409814e-01,  1.25353164e+00],
       [-1.69233074e-01, -1.80812471e+00],
       [ 1.96238569e+00, -1.16735314e+00],
       [-6.29577669e-02,  3.35455389e-01],
       [-1.53546477e+00, -2.41605843e-01],
       [ 4.30938340e+00, -2.35132954e+00],
       [-6.06903926e-01, -4.70377067e-01],
       [ 2.21661329e+00,  9.71933747e-02],
       [ 3.04282014e+00,  2.18979891e+00],
       [ 3.69014412e+00, -1.82136640e+00],
       [ 2.70559816e-01, -1.65938255e-01],
       [ 4.10394136e+00, -1.54186225e+00],
       [ 1.51559284e+00,  3.46707858e-01],
       [-8.04695267e-01, -2.24758468e-01],
       [-1.85885629e+00, -5.59831066e-01],
       [-5.16854087e-01,  4.33392141e-01],
       [ 1.13882246e+00,  2.17890716e+00],
       [-1.39428244e+00, -1.47420177e-01],
       [ 3.21042374e+00, -1.56765882e+00],
       [-1.25185050e+00,  3.46940652e+00],
       [ 2.24438129e+00, -1.28232515e+00],
       [ 3.08746154e+00, -1.39500902e+00],
       [-1.57387566e+00, -8.85380807e-01],
       [-1.39187114e+00, -3.63792156e+00],
       [ 7.92013667e-01,  4.89656935e-01],
       [ 3.50848030e+00, -2.00133849e+00],
       [ 1.33171351e+00, -6.16441186e-01],
       [ 4.95652861e-01, -4.82143161e-01],
       [ 6.82196734e-01,  6.40091568e-01],
       [ 2.43517043e+00,  2.08166961e+00],
       [-1.86339010e-01,  4.21944167e+00],
       [-1.26953837e+00,  2.22652917e-01],
       [-1.81933604e+00, -1.06303748e+00],
       [ 1.00215048e+00, -1.88726277e-01],
       [ 1.69471429e+00, -1.65805262e+00],
       [-5.96741407e-01, -4.87401971e-01],
       [-1.12219184e+00, -3.50511741e-01],
       [-1.92214393e+00,  1.98740755e+00],
       [ 9.89079473e-01, -2.52507760e+00],
       [-4.07818180e+00, -2.53975908e+00],
       [ 3.96706220e+00, -7.72726037e-01],
       [ 1.81558040e+00,  3.98626479e-01],
       [ 2.79866636e+00, -1.57846739e+00],
       [ 5.92221987e-03, -4.41519810e-01],
       [-9.17712349e-01, -3.95711611e-01],
       [-1.62761760e+00,  4.33091103e-02],
       [ 5.82477554e+00, -2.83478642e+00],
       [ 1.26104474e+00,  2.16293750e+00],
       [ 6.88904705e-02, -5.90563937e-01],
       [ 3.84324643e+00,  8.76573173e-02],
       [-1.52897659e+00, -1.04287007e+00],
       [-1.63584542e+00, -1.87040630e+00],
       [-1.57286381e+00, -4.07094360e-01],
       [ 4.08491227e-01,  1.44140746e+00],
       [-1.31881495e+00, -2.21994897e+00],
       [ 3.38472766e-02,  4.47504802e+00],
       [ 6.91831989e-01, -3.41069016e-01],
       [ 2.86225423e+00, -4.35290287e-01],
       [-1.40809999e-01, -1.40453530e-01],
       [ 2.59674308e+00, -1.79098849e+00],
       [-1.47732777e+00,  4.37659131e+00],
       [ 6.68796026e-01, -2.50680677e+00],
       [-1.01866194e+00, -2.02094156e+00],
       [-3.96294287e-01,  1.24949178e+00],
       [-1.99802979e+00, -1.52843057e+00],
       [-4.08560905e+00,  2.78063063e+00],
       [ 5.56780091e-01, -3.42487408e-01],
       [ 1.08087535e+00, -2.82120464e-01],
       [ 1.04948785e+00,  2.11237749e+00],
       [-1.46748145e+00, -1.04283228e+00],
       [-7.16890614e-01, -1.18109243e-01],
       [-3.29405618e+00, -1.15114457e+00],
       [ 2.32363722e-01, -1.31241236e+00],
       [ 2.09057768e-01, -2.73220820e+00],
       [ 1.76339172e+00, -1.51527341e+00],
       [ 1.67366165e+00,  1.17810767e+00],
       [-6.56474627e-02, -7.81406720e-01],
       [-4.75619311e+00,  2.14778204e-01],
       [-3.50282301e+00, -9.20896167e-01],
       [-7.29122761e-01,  7.24071637e-01],
       [ 3.50887188e-02, -2.84150837e+00],
       [-1.70156673e+00, -5.15768821e-01],
       [ 1.53363191e+00,  1.08950182e+00],
       [ 1.92909456e+00, -1.36937232e+00],
       [ 7.98320198e-01, -1.57284625e+00],
       [-1.20713101e+00,  2.51696638e+00],
       [ 1.59017572e+00, -6.60777853e-01],
       [ 7.54503398e-01, -1.89400649e+00],
       [-4.24469851e+00, -5.01354797e-01],
       [-9.73608007e-02,  2.13295477e+00],
       [-1.00284689e+00, -5.86631925e-01],
       [ 2.77778877e+00, -7.47727499e-01],
       [-1.42602324e+00, -1.04680294e+00],
       [-2.41288072e+00,  1.53198863e+00],
       [-3.30543565e+00,  3.99574493e-01],
       [ 8.09378577e-01, -2.33350508e+00],
       [ 7.93951689e-01,  3.91151228e+00],
       [-3.80398929e+00,  7.64371361e-01],
       [ 2.17068901e-01, -5.57936077e-01],
       [ 4.46538066e-01, -4.46126921e-01],
       [-3.76520311e-01, -8.54731945e-01],
       [ 9.28566985e-01, -2.79685321e+00],
       [ 2.12052130e-01, -1.64326002e+00],
       [ 2.81943563e+00, -1.45368676e-01],
       [-4.62940328e-01, -1.22356111e+00],
       [-1.14873835e+00, -1.95487362e+00],
       [ 3.17335534e+00,  2.93056829e-01],
       [ 2.62183971e+00, -1.60707392e+00],
       [-2.31105104e-01,  4.21899933e-01],
       [ 2.17332469e-01, -1.01486682e-01],
       [-8.96745989e-01, -1.61273816e-01],
       [ 1.66338964e+00, -3.28957830e-02],
       [ 3.99640621e+00,  3.39454129e+00],
       [ 5.64066166e+00,  1.21628484e+00],
       [ 5.57799915e-01,  1.26861624e-01],
       [-4.57856779e-02, -1.09273522e+00],
       [ 2.57489940e+00, -1.40230030e+00],
       [-1.21918775e-02,  1.29147714e+00],
       [-2.74383615e+00, -8.62573830e-01],
       [ 3.91191053e+00,  1.31948002e+00],
       [ 8.73179852e-02, -1.11242976e+00],
       [-2.20054456e+00, -1.20815707e+00],
       [-1.58233779e+00,  9.18079330e-02],
       [-2.38355150e+00, -8.34470636e-01],
       [ 5.49452416e-02, -2.21502808e+00],
       [ 1.97079928e+00,  1.00809263e+00],
       [ 1.31037255e+00,  2.23880046e-01],
       [-3.98499143e+00, -6.16605614e-01],
       [ 1.09099656e+00,  1.53979631e+00],
       [ 1.19287725e-01, -4.72687656e-01],
       [ 1.49249568e+00,  4.40674660e-01],
       [ 1.31982908e+00,  4.72695079e-01],
       [ 4.44789138e+00,  1.40907860e+00],
       [-1.23409599e+00,  1.45572599e+00],
       [-2.64026037e+00, -6.19356619e-01],
       [ 2.15796524e+00,  1.43985214e-01],
       [ 1.84073279e+00,  1.43395998e+00],
       [ 6.06253113e-01, -6.42431247e-01],
       [-1.22734384e+00, -1.72533409e+00],
       [-2.82398401e+00,  1.54302468e+00],
       [-2.35682173e+00,  1.30272353e+00],
       [ 2.84583603e+00,  1.32717608e-01],
       [ 3.18004639e+00, -1.91290875e-01]])

################################################# Linear Regression ###########################################################

## Import linear regression model library.
from sklearn.linear_model import LinearRegression

## Instantiate regression model and fit  a model.
linreg=LinearRegression()
linear_model=linreg.fit(train_data_final,y_train)

## Get the predictions on train and validation data.
pred_train = linear_model.predict(train_data_final)
pred_test = linear_model.predict(test_data_final)

## Get predictions on test data.
test_pred = linear_model.predict(test_data_combine)

## Display RMSE value for train and validation data.
print("Train Error:",sqrt(mean_squared_error(y_train, pred_train)))
print("Test Error:",sqrt(mean_squared_error(y_test, pred_test)))

Train Error: 18987.647924827896
Test Error: 213230686032402.3

#There is an indication given in the result that there might exist a strong multicollinearity in the data. 
#Lets use variance inflation factor (VIF) to understand if there exist a multicollinearity and remove those attributes.

## Import VIF library and get VIF vallues for  train data.
from statsmodels.stats.outliers_influence import variance_inflation_factor
vif=pd.DataFrame()
vif['Vif']=[variance_inflation_factor(train_data_final.values,i) for i in range(train_data_final.shape[1])]
vif['Variables']=train_data_final.columns.values

C:\Users\nagar\Anaconda3\lib\site-packages\statsmodels\stats\outliers_influence.py:181: RuntimeWarning: divide by zero encountered in double_scalars
  vif = 1. / (1. - r_squared_i)

## Display VIF values for train data.
vif

############@@@@@@@@@########## Perform Grid Search,Ridge,Lasso ###############################################################

## Import Ridge,lasso model libraires.
from sklearn.linear_model import Ridge, Lasso

## Ridge

## Import Grid search library.
from sklearn.model_selection import GridSearchCV 
## Ridge regression is parametric and takes a parameter alpha. The value of alpha determines the reduction in magnitude of coefficients.
## But we also need to check which value of alpha gives best predictions on test data. For this we experiment with several values of alpha and pick the best
## We do this by performing grid search over several values of alpha. 
alphas = np.array([1,0.1,0.01,0.001,0.0001,0,1.5,2]) ## Pick the best of these values.
## Create and fit a ridge regression model, testing each alpha.
model_ridge = Ridge()
grid = GridSearchCV(estimator=model_ridge, param_grid=dict(alpha=alphas),cv=10) ## Here the argument cv=10 implies compute error on 10 chucks of data and report average value.
grid.fit(train_data_final,y_train)
print(grid)

GridSearchCV(cv=10, error_score=nan,
             estimator=Ridge(alpha=1.0, copy_X=True, fit_intercept=True,
                             max_iter=None, normalize=False, random_state=None,
                             solver='auto', tol=0.001),
             iid='deprecated', n_jobs=None,
             param_grid={'alpha': array([1.0e+00, 1.0e-01, 1.0e-02, 1.0e-03, 1.0e-04, 0.0e+00, 1.5e+00,
       2.0e+00])},
             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,
             scoring=None, verbose=0)

## Print best params.
print(grid.best_score_)
print(grid.best_estimator_.alpha)

0.7608287095091045
2.0

## Instantiate Ridge and fit it.
Ridge_model= Ridge(alpha=2,normalize=False)
Ridge_model.fit(train_data_final,y_train) ## Applying it on the train data, to obtain the coefficients.

Ridge(alpha=2, copy_X=True, fit_intercept=True, max_iter=None, normalize=False,
      random_state=None, solver='auto', tol=0.001)

## Get the predictions on train and validation data.
pred_train = Ridge_model.predict(train_data_final)
pred_test = Ridge_model.predict(test_data_final)

## Get predictions on test data.
test_pred = Ridge_model.predict(test_data_combine)

## Display RMSE value for train and validation data.
print("Train Error:",sqrt(mean_squared_error(y_train, pred_train)))
print("Test Error:",sqrt(mean_squared_error(y_test, pred_test)))

Train Error: 23581.943193260486
Test Error: 38789.74028716411

## Lasso

## Get best parameter vlaues by doing grid search.
model_lasso = Lasso()
grid = GridSearchCV(estimator=model_lasso, param_grid=dict(alpha=alphas),cv=10) #Here the argument cv=10 implies compute error on 10 chucks of data and report average value
grid.fit(train_data_final,y_train)
print(grid)

C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 132283552276.12665, tolerance: 586416997.6105675
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 151663669283.90045, tolerance: 600453299.974236
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 158740041164.45273, tolerance: 620994499.8141209
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 123344376302.26239, tolerance: 615809968.3676016
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 140781385865.65784, tolerance: 568848936.1139773
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 156195508585.75662, tolerance: 595517884.9173822
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 158502548646.73212, tolerance: 621049274.6193085
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 162310583572.46268, tolerance: 599486045.6069565
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13517905993.891144, tolerance: 600818404.6004866
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 165010261997.6953, tolerance: 612724923.8869787
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 135500461747.66463, tolerance: 586416997.6105675
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 155076158397.17624, tolerance: 600453299.974236
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 168542377853.999, tolerance: 620994499.8141209
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 125737659894.84619, tolerance: 615809968.3676016
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 147776486107.88174, tolerance: 568848936.1139773
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 159157662341.10913, tolerance: 595517884.9173822
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 161191554269.7159, tolerance: 621049274.6193085
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 165532344219.9775, tolerance: 599486045.6069565
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 153640045858.81238, tolerance: 600818404.6004866
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 168866180257.60345, tolerance: 612724923.8869787
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 136191512314.9615, tolerance: 586416997.6105675
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 155768556412.25137, tolerance: 600453299.974236
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 169282554570.844, tolerance: 620994499.8141209
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 126161299358.95221, tolerance: 615809968.3676016
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 157142358721.08127, tolerance: 568848936.1139773
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 159748079904.3531, tolerance: 595517884.9173822
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 161735022525.51306, tolerance: 621049274.6193085
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 166155175440.84982, tolerance: 599486045.6069565
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 167401858539.57175, tolerance: 600818404.6004866
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 169619094896.12466, tolerance: 612724923.8869787
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 136266512425.06892, tolerance: 586416997.6105675
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 155851191765.648, tolerance: 600453299.974236
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 169358827229.7449, tolerance: 620994499.8141209
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 126211912716.69667, tolerance: 615809968.3676016
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 159633335235.47568, tolerance: 568848936.1139773
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 159817012919.41357, tolerance: 595517884.9173822
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 161798971794.1304, tolerance: 621049274.6193085
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 166227498302.9395, tolerance: 599486045.6069565
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 168726366547.3222, tolerance: 600818404.6004866
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 169707860342.087, tolerance: 612724923.8869787
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 136274057865.86304, tolerance: 586416997.6105675
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 155859610588.30753, tolerance: 600453299.974236
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 169366475270.95752, tolerance: 620994499.8141209
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 126217069343.087, tolerance: 615809968.3676016
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 159878364429.8121, tolerance: 568848936.1139773
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 159824010387.80765, tolerance: 595517884.9173822
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 161805476601.50433, tolerance: 621049274.6193085
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 166234845671.58163, tolerance: 599486045.6069565
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 168858320100.98807, tolerance: 600818404.6004866
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 169716893665.36902, tolerance: 612724923.8869787
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:515: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator
  estimator.fit(X_train, y_train, **fit_params)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 136274896570.39502, tolerance: 586416997.6105675
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:515: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator
  estimator.fit(X_train, y_train, **fit_params)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 155860547770.64722, tolerance: 600453299.974236
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:515: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator
  estimator.fit(X_train, y_train, **fit_params)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 169367325036.09665, tolerance: 620994499.8141209
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:515: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator
  estimator.fit(X_train, y_train, **fit_params)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 126217643387.33908, tolerance: 615809968.3676016
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:515: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator
  estimator.fit(X_train, y_train, **fit_params)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 159905544326.53275, tolerance: 568848936.1139773
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:515: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator
  estimator.fit(X_train, y_train, **fit_params)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 159824788664.6952, tolerance: 595517884.9173822
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:515: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator
  estimator.fit(X_train, y_train, **fit_params)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 161806200612.03137, tolerance: 621049274.6193085
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:515: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator
  estimator.fit(X_train, y_train, **fit_params)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 166235663357.0879, tolerance: 599486045.6069565
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:515: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator
  estimator.fit(X_train, y_train, **fit_params)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 168872976003.698, tolerance: 600818404.6004866
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:515: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator
  estimator.fit(X_train, y_train, **fit_params)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 169717899135.38824, tolerance: 612724923.8869787
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 131917434426.06377, tolerance: 586416997.6105675
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 151619836495.39517, tolerance: 600453299.974236
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 149420849916.41656, tolerance: 620994499.8141209
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 123406506682.24747, tolerance: 615809968.3676016
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 143205247619.85175, tolerance: 568848936.1139773
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 156085028299.17822, tolerance: 595517884.9173822
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 158535821556.43405, tolerance: 621049274.6193085
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 162112141735.13416, tolerance: 599486045.6069565
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2248601315.1430054, tolerance: 600818404.6004866
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 164730061152.02676, tolerance: 612724923.8869787
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 132097157478.19542, tolerance: 586416997.6105675
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 152040330790.3219, tolerance: 600453299.974236
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 135269185357.28479, tolerance: 620994499.8141209
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 123909094784.74976, tolerance: 615809968.3676016
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 145432817081.4537, tolerance: 568848936.1139773
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 156409232806.73566, tolerance: 595517884.9173822
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 158987043816.8599, tolerance: 621049274.6193085
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 162330488914.3558, tolerance: 599486045.6069565
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1576156088.5111694, tolerance: 600818404.6004866
  positive)
C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 164960209951.37195, tolerance: 612724923.8869787
  positive)

GridSearchCV(cv=10, error_score=nan,
             estimator=Lasso(alpha=1.0, copy_X=True, fit_intercept=True,
                             max_iter=1000, normalize=False, positive=False,
                             precompute=False, random_state=None,
                             selection='cyclic', tol=0.0001, warm_start=False),
             iid='deprecated', n_jobs=None,
             param_grid={'alpha': array([1.0e+00, 1.0e-01, 1.0e-02, 1.0e-03, 1.0e-04, 0.0e+00, 1.5e+00,
       2.0e+00])},
             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,
             scoring=None, verbose=0)

C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 181238574198.1571, tolerance: 669162392.3363616
  positive)

## Display  best parameters.
print(grid.best_score_)
print(grid.best_estimator_.alpha)

0.6587272788673987
1.0

## Instantiate Lasso and fit it.
Lasso_model= Lasso(alpha=1.0,normalize=False)
Lasso_model.fit(train_data_final,y_train) ## Applying it on the train data, to obtain the coefficients.

C:\Users\nagar\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 181238574198.1571, tolerance: 669162392.3363616
  positive)

Lasso(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=1000,
      normalize=False, positive=False, precompute=False, random_state=None,
      selection='cyclic', tol=0.0001, warm_start=False)

## Get the predictions on train and validation data.
pred_train = Lasso_model.predict(train_data_final)
pred_test = Lasso_model.predict(test_data_final)

## Get predictions on test data.
test_pred = Lasso_model.predict(test_data_combine)

## Display RMSE value for train and validation data.
print("Train Error:",sqrt(mean_squared_error(y_train, pred_train)))
print("Test Error:",sqrt(mean_squared_error(y_test, pred_test)))

Train Error: 19105.464258594904
Test Error: 260630.3318911821

	MSZoning	Street	Alley	LotShape	LandContour	Utilities	LotConfig	LandSlope	Neighborhood	Condition1	Condition2	BldgType	HouseStyle	RoofStyle	RoofMatl	Exterior1st	Exterior2nd	MasVnrType	ExterQual	ExterCond	Foundation	BsmtQual	BsmtCond	BsmtExposure	BsmtFinType1	BsmtFinType2	Heating	HeatingQC	CentralAir	Electrical	KitchenQual	Functional	FireplaceQu	GarageType	GarageFinish	GarageQual	GarageCond	PavedDrive	PoolQC	Fence	MiscFeature	SaleType	SaleCondition
0	RL	Pave	NAA	Reg	Lvl	AllPub	Inside	Gtl	CollgCr	Norm	Norm	1Fam	2Story	Gable	CompShg	VinylSd	VinylSd	BrkFace	Gd	TA	PConc	Gd	TA	No	GLQ	Unf	GasA	Ex	Y	SBrkr	Gd	Typ	NF	Attchd	RFn	TA	TA	Y	NP	NF	NE	WD	Normal
1	RL	Pave	NAA	Reg	Lvl	AllPub	FR2	Gtl	Veenker	Feedr	Norm	1Fam	1Story	Gable	CompShg	MetalSd	MetalSd	None	TA	TA	CBlock	Gd	TA	Gd	ALQ	Unf	GasA	Ex	Y	SBrkr	TA	Typ	TA	Attchd	RFn	TA	TA	Y	NP	NF	NE	WD	Normal
2	RL	Pave	NAA	IR1	Lvl	AllPub	Inside	Gtl	CollgCr	Norm	Norm	1Fam	2Story	Gable	CompShg	VinylSd	VinylSd	BrkFace	Gd	TA	PConc	Gd	TA	Mn	GLQ	Unf	GasA	Ex	Y	SBrkr	Gd	Typ	TA	Attchd	RFn	TA	TA	Y	NP	NF	NE	WD	Normal
3	RL	Pave	NAA	IR1	Lvl	AllPub	Corner	Gtl	Crawfor	Norm	Norm	1Fam	2Story	Gable	CompShg	Wd Sdng	Wd Shng	None	TA	TA	BrkTil	TA	Gd	No	ALQ	Unf	GasA	Gd	Y	SBrkr	Gd	Typ	Gd	Detchd	Unf	TA	TA	Y	NP	NF	NE	WD	Abnorml
4	RL	Pave	NAA	IR1	Lvl	AllPub	FR2	Gtl	NoRidge	Norm	Norm	1Fam	2Story	Gable	CompShg	VinylSd	VinylSd	BrkFace	Gd	TA	PConc	Gd	TA	Av	GLQ	Unf	GasA	Ex	Y	SBrkr	Gd	Typ	TA	Attchd	RFn	TA	TA	Y	NP	NF	NE	WD	Normal

	LotFrontage	LotArea	YearBuilt	YearRemodAdd	MasVnrArea	BsmtFinSF1	BsmtFinSF2	BsmtUnfSF	TotalBsmtSF	1stFlrSF	2ndFlrSF	LowQualFinSF	GrLivArea	BsmtFullBath	BsmtHalfBath	FullBath	HalfBath	BedroomAbvGr	KitchenAbvGr	TotRmsAbvGrd	Fireplaces	GarageYrBlt	GarageCars	GarageArea	WoodDeckSF	OpenPorchSF	EnclosedPorch	3SsnPorch	ScreenPorch	PoolArea	MiscVal	MoSold	YrSold	SalePrice
LotFrontage	1.000000	0.426095	0.123349	0.088866	0.193458	0.233633	0.049900	0.132644	0.392075	0.457181	0.080177	0.038469	0.402797	0.100949	-0.007234	0.198769	0.053532	0.263170	-0.006069	0.352096	0.266639	0.070250	0.285691	0.344997	0.088521	0.151972	0.010700	0.070029	0.041383	0.206167	0.003368	0.011200	0.007450	0.351799
LotArea	0.426095	1.000000	0.014228	0.013788	0.104160	0.214103	0.111170	-0.002618	0.260833	0.299475	0.050986	0.004779	0.263116	0.158155	0.048046	0.126031	0.014259	0.119690	-0.017784	0.190015	0.271364	-0.024947	0.154871	0.180403	0.171698	0.084774	-0.018340	0.020423	0.043160	0.077672	0.038068	0.001205	-0.014261	0.263843
YearBuilt	0.123349	0.014228	1.000000	0.592855	0.315707	0.249503	-0.049107	0.149040	0.391452	0.281986	0.010308	-0.183784	0.199010	0.187599	-0.038162	0.468271	0.242656	-0.070651	-0.174800	0.095589	0.147716	0.825667	0.537850	0.478954	0.224880	0.188686	-0.387268	0.031355	-0.050364	0.004950	-0.034383	0.012398	-0.013618	0.522897
YearRemodAdd	0.088866	0.013788	0.592855	1.000000	0.179618	0.128451	-0.067759	0.181133	0.291066	0.240379	0.140024	-0.062419	0.287389	0.119470	-0.012337	0.439046	0.183331	-0.040581	-0.149598	0.191740	0.112581	0.642277	0.420622	0.371600	0.205726	0.226298	-0.193919	0.045286	-0.038740	0.005829	-0.010286	0.021490	0.035743	0.507101
MasVnrArea	0.193458	0.104160	0.315707	0.179618	1.000000	0.264736	-0.072319	0.114442	0.363936	0.344501	0.174561	-0.069071	0.390857	0.085310	0.026673	0.276833	0.201444	0.102821	-0.037610	0.280682	0.249070	0.252691	0.364204	0.373066	0.159718	0.125703	-0.110204	0.018796	0.061466	0.011723	-0.029815	-0.005965	-0.008201	0.477493
BsmtFinSF1	0.233633	0.214103	0.249503	0.128451	0.264736	1.000000	-0.050117	-0.495251	0.522396	0.445863	-0.137079	-0.064503	0.208171	0.649212	0.067418	0.058543	0.004262	-0.107355	-0.081007	0.044316	0.260011	0.153484	0.224054	0.296970	0.204306	0.111761	-0.102303	0.026451	0.062021	0.140491	0.003571	-0.015727	0.014359	0.386420
BsmtFinSF2	0.049900	0.111170	-0.049107	-0.067759	-0.072319	-0.050117	1.000000	-0.209294	0.104810	0.097117	-0.099260	0.014807	-0.009640	0.158678	0.070948	-0.076444	-0.032148	-0.015728	-0.040751	-0.035227	0.046921	-0.088011	-0.038264	-0.018227	0.067898	0.003093	0.036543	-0.029993	0.088871	0.041709	0.004940	-0.015211	0.031706	-0.011378
BsmtUnfSF	0.132644	-0.002618	0.149040	0.181133	0.114442	-0.495251	-0.209294	1.000000	0.415360	0.317987	0.004469	0.028167	0.240257	-0.422900	-0.095804	0.288886	-0.041118	0.166643	0.030086	0.250647	0.051575	0.190708	0.214175	0.183303	-0.005316	0.129005	-0.002538	0.020764	-0.012579	-0.035092	-0.023837	0.034888	-0.041258	0.214479
TotalBsmtSF	0.392075	0.260833	0.391452	0.291066	0.363936	0.522396	0.104810	0.415360	1.000000	0.819530	-0.174512	-0.033245	0.454868	0.307351	-0.000315	0.323722	-0.048804	0.050450	-0.068901	0.285573	0.339519	0.322445	0.434585	0.486665	0.232019	0.247264	-0.095478	0.037384	0.084489	0.126053	-0.018479	0.013196	-0.014969	0.613581
1stFlrSF	0.457181	0.299475	0.281986	0.240379	0.344501	0.445863	0.097117	0.317987	0.819530	1.000000	-0.202646	-0.014241	0.566024	0.244671	0.001956	0.380637	-0.119916	0.127401	0.068101	0.409516	0.410531	0.233449	0.439317	0.489782	0.235459	0.211671	-0.065292	0.056104	0.088758	0.131525	-0.021096	0.031372	-0.013604	0.605852
2ndFlrSF	0.080177	0.050986	0.010308	0.140024	0.174561	-0.137079	-0.099260	0.004469	-0.174512	-0.202646	1.000000	0.063353	0.687501	-0.169494	-0.023855	0.421378	0.609707	0.502901	0.059306	0.616423	0.194561	0.070832	0.183926	0.138347	0.092165	0.208026	0.061989	-0.024358	0.040606	0.081487	0.016197	0.035164	-0.028700	0.319334
LowQualFinSF	0.038469	0.004779	-0.183784	-0.062419	-0.069071	-0.064503	0.014807	0.028167	-0.033245	-0.014241	0.063353	1.000000	0.134683	-0.047143	-0.005842	-0.000710	-0.027080	0.105607	0.007522	0.131185	-0.021272	-0.036363	-0.094480	-0.067601	-0.025444	0.018251	0.061081	-0.004296	0.026799	0.062157	-0.003793	-0.022174	-0.028921	-0.025606
GrLivArea	0.402797	0.263116	0.199010	0.287389	0.390857	0.208171	-0.009640	0.240257	0.454868	0.566024	0.687501	0.134683	1.000000	0.034836	-0.018918	0.630012	0.415772	0.521270	0.100063	0.825489	0.461679	0.231197	0.467247	0.468997	0.247433	0.330224	0.009113	0.020643	0.101510	0.170205	-0.002416	0.050240	-0.036526	0.708624
BsmtFullBath	0.100949	0.158155	0.187599	0.119470	0.085310	0.649212	0.158678	-0.422900	0.307351	0.244671	-0.169494	-0.047143	0.034836	1.000000	-0.147871	-0.064512	-0.030905	-0.150673	-0.041503	-0.053275	0.137928	0.124553	0.131881	0.179189	0.175315	0.067341	-0.049911	-0.000106	0.023148	0.067616	-0.023047	-0.025361	0.067049	0.227122
BsmtHalfBath	-0.007234	0.048046	-0.038162	-0.012337	0.026673	0.067418	0.070948	-0.095804	-0.000315	0.001956	-0.023855	-0.005842	-0.018918	-0.147871	1.000000	-0.054536	-0.012340	0.046519	-0.037944	-0.023836	0.028976	-0.077464	-0.020891	-0.024536	0.040161	-0.025324	-0.008555	0.035114	0.032121	0.020025	-0.007367	0.032873	-0.046524	-0.016844
FullBath	0.198769	0.126031	0.468271	0.439046	0.276833	0.058543	-0.076444	0.288886	0.323722	0.380637	0.421378	-0.000710	0.630012	-0.064512	-0.054536	1.000000	0.136381	0.363252	0.133115	0.554784	0.243671	0.484557	0.469672	0.405656	0.187703	0.259977	-0.115093	0.035353	-0.008106	0.049604	-0.014290	0.055872	-0.019669	0.560664
HalfBath	0.053532	0.014259	0.242656	0.183331	0.201444	0.004262	-0.032148	-0.041118	-0.048804	-0.119916	0.609707	-0.027080	0.415772	-0.030905	-0.012340	0.136381	1.000000	0.226651	-0.068263	0.343415	0.203649	0.196785	0.219178	0.163549	0.108080	0.199740	-0.095317	-0.004972	0.072426	0.022381	0.001290	-0.009050	-0.010269	0.284108
BedroomAbvGr	0.263170	0.119690	-0.070651	-0.040581	0.102821	-0.107355	-0.015728	0.166643	0.050450	0.127401	0.502901	0.105607	0.521270	-0.150673	0.046519	0.363252	0.226651	1.000000	0.198597	0.676620	0.107570	-0.064518	0.086106	0.065253	0.046854	0.093810	0.041570	-0.024478	0.044300	0.070703	0.007767	0.046544	-0.036014	0.168213
KitchenAbvGr	-0.006069	-0.017784	-0.174800	-0.149598	-0.037610	-0.081007	-0.040751	0.030086	-0.068901	0.068101	0.059306	0.007522	0.100063	-0.041503	-0.037944	0.133115	-0.068263	0.198597	1.000000	0.256045	-0.123936	-0.124411	-0.050634	-0.064433	-0.090130	-0.070091	0.037312	-0.024600	-0.051613	-0.014525	0.062341	0.026589	0.031687	-0.135907
TotRmsAbvGrd	0.352096	0.190015	0.095589	0.191740	0.280682	0.044316	-0.035227	0.250647	0.285573	0.409516	0.616423	0.131185	0.825489	-0.053275	-0.023836	0.554784	0.343415	0.676620	0.256045	1.000000	0.326114	0.148112	0.362289	0.337822	0.165984	0.234192	0.004151	-0.006683	0.059383	0.083757	0.024763	0.036907	-0.034516	0.533723
Fireplaces	0.266639	0.271364	0.147716	0.112581	0.249070	0.260011	0.046921	0.051575	0.339519	0.410531	0.194561	-0.021272	0.461679	0.137928	0.028976	0.243671	0.203649	0.107570	-0.123936	0.326114	1.000000	0.046822	0.300789	0.269141	0.200019	0.169405	-0.024822	0.011257	0.184530	0.095074	0.001409	0.046357	-0.024096	0.466929
GarageYrBlt	0.070250	-0.024947	0.825667	0.642277	0.252691	0.153484	-0.088011	0.190708	0.322445	0.233449	0.070832	-0.036363	0.231197	0.124553	-0.077464	0.484557	0.196785	-0.064518	-0.124411	0.148112	0.046822	1.000000	0.588920	0.564567	0.224577	0.228425	-0.297003	0.023544	-0.075418	-0.014501	-0.032417	0.005337	-0.001014	0.486362
GarageCars	0.285691	0.154871	0.537850	0.420622	0.364204	0.224054	-0.038264	0.214175	0.434585	0.439317	0.183926	-0.094480	0.467247	0.131881	-0.020891	0.469672	0.219178	0.086106	-0.050634	0.362289	0.300789	0.588920	1.000000	0.882475	0.226342	0.213569	-0.151434	0.035765	0.050494	0.020934	-0.043080	0.040522	-0.039117	0.640409
GarageArea	0.344997	0.180403	0.478954	0.371600	0.373066	0.296970	-0.018227	0.183303	0.486665	0.489782	0.138347	-0.067601	0.468997	0.179189	-0.024536	0.405656	0.163549	0.065253	-0.064433	0.337822	0.269141	0.564567	0.882475	1.000000	0.224666	0.241435	-0.121777	0.035087	0.051412	0.061047	-0.027400	0.027974	-0.027378	0.623431
WoodDeckSF	0.088521	0.171698	0.224880	0.205726	0.159718	0.204306	0.067898	-0.005316	0.232019	0.235459	0.092165	-0.025444	0.247433	0.175315	0.040161	0.187703	0.108080	0.046854	-0.090130	0.165984	0.200019	0.224577	0.226342	0.224666	1.000000	0.058661	-0.125989	-0.032771	-0.074181	0.073378	-0.009551	0.021011	0.022270	0.324413
OpenPorchSF	0.151972	0.084774	0.188686	0.226298	0.125703	0.111761	0.003093	0.129005	0.247264	0.211671	0.208026	0.018251	0.330224	0.067341	-0.025324	0.259977	0.199740	0.093810	-0.070091	0.234192	0.169405	0.228425	0.213569	0.241435	0.058661	1.000000	-0.093079	-0.005842	0.074304	0.060762	-0.018584	0.071255	-0.057619	0.315856
EnclosedPorch	0.010700	-0.018340	-0.387268	-0.193919	-0.110204	-0.102303	0.036543	-0.002538	-0.095478	-0.065292	0.061989	0.061081	0.009113	-0.049911	-0.008555	-0.115093	-0.095317	0.041570	0.037312	0.004151	-0.024822	-0.297003	-0.151434	-0.121777	-0.125989	-0.093079	1.000000	-0.037305	-0.082864	0.054203	0.018361	-0.028887	-0.009916	-0.128578
3SsnPorch	0.070029	0.020423	0.031355	0.045286	0.018796	0.026451	-0.029993	0.020764	0.037384	0.056104	-0.024358	-0.004296	0.020643	-0.000106	0.035114	0.035353	-0.004972	-0.024478	-0.024600	-0.006683	0.011257	0.023544	0.035765	0.035087	-0.032771	-0.005842	-0.037305	1.000000	-0.031436	-0.007992	0.000354	0.029474	0.018645	0.044584
ScreenPorch	0.041383	0.043160	-0.050364	-0.038740	0.061466	0.062021	0.088871	-0.012579	0.084489	0.088758	0.040606	0.026799	0.101510	0.023148	0.032121	-0.008106	0.072426	0.044300	-0.051613	0.059383	0.184530	-0.075418	0.050494	0.051412	-0.074181	0.074304	-0.082864	-0.031436	1.000000	0.051307	0.031946	0.023217	0.010694	0.111447
PoolArea	0.206167	0.077672	0.004950	0.005829	0.011723	0.140491	0.041709	-0.035092	0.126053	0.131525	0.081487	0.062157	0.170205	0.067616	0.020025	0.049604	0.022381	0.070703	-0.014525	0.083757	0.095074	-0.014501	0.020934	0.061047	0.073378	0.060762	0.054203	-0.007992	0.051307	1.000000	0.029669	-0.033737	-0.059689	0.092404
MiscVal	0.003368	0.038068	-0.034383	-0.010286	-0.029815	0.003571	0.004940	-0.023837	-0.018479	-0.021096	0.016197	-0.003793	-0.002416	-0.023047	-0.007367	-0.014290	0.001290	0.007767	0.062341	0.024763	0.001409	-0.032417	-0.043080	-0.027400	-0.009551	-0.018584	0.018361	0.000354	0.031946	0.029669	1.000000	-0.006495	0.004906	-0.021190
MoSold	0.011200	0.001205	0.012398	0.021490	-0.005965	-0.015727	-0.015211	0.034888	0.013196	0.031372	0.035164	-0.022174	0.050240	-0.025361	0.032873	0.055872	-0.009050	0.046544	0.026589	0.036907	0.046357	0.005337	0.040522	0.027974	0.021011	0.071255	-0.028887	0.029474	0.023217	-0.033737	-0.006495	1.000000	-0.145721	0.046432
YrSold	0.007450	-0.014261	-0.013618	0.035743	-0.008201	0.014359	0.031706	-0.041258	-0.014969	-0.013604	-0.028700	-0.028921	-0.036526	0.067049	-0.046524	-0.019669	-0.010269	-0.036014	0.031687	-0.034516	-0.024096	-0.001014	-0.039117	-0.027378	0.022270	-0.057619	-0.009916	0.018645	0.010694	-0.059689	0.004906	-0.145721	1.000000	-0.028923
SalePrice	0.351799	0.263843	0.522897	0.507101	0.477493	0.386420	-0.011378	0.214479	0.613581	0.605852	0.319334	-0.025606	0.708624	0.227122	-0.016844	0.560664	0.284108	0.168213	-0.135907	0.533723	0.466929	0.486362	0.640409	0.623431	0.324413	0.315856	-0.128578	0.044584	0.111447	0.092404	-0.021190	0.046432	-0.028923	1.000000

	LotFrontage	LotArea	YearBuilt	YearRemodAdd	MasVnrArea	BsmtFinSF1	BsmtFinSF2	BsmtUnfSF	TotalBsmtSF	1stFlrSF	2ndFlrSF	LowQualFinSF	GrLivArea	BsmtFullBath	BsmtHalfBath	FullBath	HalfBath	BedroomAbvGr	KitchenAbvGr	TotRmsAbvGrd	Fireplaces	GarageYrBlt	GarageCars	GarageArea	WoodDeckSF	OpenPorchSF	EnclosedPorch	3SsnPorch	ScreenPorch	PoolArea	MiscVal	MoSold	YrSold
LotFrontage	1.000000	0.644608	0.122356	0.092603	0.251533	0.204621	0.046824	0.092031	0.315802	0.461239	-0.036185	-0.037294	0.357125	0.127314	-0.042779	0.163078	0.023850	0.205100	0.016072	0.344366	0.257037	0.082069	0.336373	0.375581	0.157426	0.179795	0.013340	-0.037487	0.113444	0.134232	0.068161	0.008810	-0.025263
LotArea	0.644608	1.000000	0.048314	0.036907	0.188691	0.185470	0.054199	0.071681	0.283049	0.456417	-0.007862	-0.012457	0.366324	0.094052	-0.008378	0.147871	0.079581	0.181171	-0.031830	0.289576	0.282210	0.018330	0.263398	0.315841	0.158483	0.164815	0.099850	-0.001846	0.088712	0.140494	0.139071	0.005152	-0.051144
YearBuilt	0.122356	0.048314	1.000000	0.631696	0.312404	0.309595	-0.008174	0.111892	0.425447	0.338733	0.025195	-0.101154	0.290412	0.234922	-0.022947	0.474028	0.296700	-0.035923	-0.098644	0.134839	0.193597	0.844150	0.538428	0.482497	0.233889	0.208040	-0.363012	-0.005442	-0.031984	-0.001060	0.007325	0.015599	-0.011006
YearRemodAdd	0.092603	0.036907	0.631696	1.000000	0.213937	0.175219	-0.056320	0.148773	0.304515	0.243793	0.177177	-0.059973	0.347946	0.150371	-0.076928	0.477064	0.238807	-0.004413	-0.135940	0.203619	0.153965	0.661765	0.431442	0.382034	0.230724	0.258049	-0.243582	0.025823	-0.053761	-0.034862	0.003011	0.011771	0.029715
MasVnrArea	0.251533	0.188691	0.312404	0.213937	1.000000	0.343267	0.037546	0.064672	0.430966	0.446875	0.063659	-0.045886	0.416648	0.198270	0.003992	0.242522	0.182094	0.053259	-0.066331	0.275533	0.301575	0.257439	0.358488	0.375182	0.172721	0.163666	-0.112814	0.005772	0.069339	-0.005395	0.105723	0.005118	-0.029556
BsmtFinSF1	0.204621	0.185470	0.309595	0.175219	0.343267	1.000000	-0.059522	-0.459581	0.550444	0.470077	-0.188952	-0.068178	0.215692	0.628903	0.088971	0.104464	-0.018966	-0.119743	-0.092190	0.060346	0.326075	0.232798	0.285959	0.323800	0.242369	0.136321	-0.097441	0.088241	0.131414	0.012089	0.165403	0.013397	0.030779
BsmtFinSF2	0.046824	0.054199	-0.008174	-0.056320	0.037546	-0.059522	1.000000	-0.265183	0.076092	0.073346	-0.095935	-0.023976	-0.025137	0.166500	0.123715	-0.074858	-0.032627	-0.044951	-0.034790	-0.060361	0.083655	-0.051545	0.005806	0.022391	0.126032	-0.014185	0.029010	-0.014473	0.039806	0.050152	-0.012808	-0.003162	-0.011749
BsmtUnfSF	0.092031	0.071681	0.111892	0.148773	0.064672	-0.459581	-0.265183	1.000000	0.409023	0.275569	-0.006196	0.067212	0.226780	-0.374659	-0.117686	0.257721	-0.030584	0.199661	0.102069	0.243566	-0.043014	0.153246	0.147005	0.145625	-0.073174	0.111249	0.012468	-0.046230	-0.085111	-0.029672	0.000320	0.009132	-0.035214
TotalBsmtSF	0.315802	0.283049	0.425447	0.304515	0.430966	0.550444	0.076092	0.409023	1.000000	0.784538	-0.238633	-0.013294	0.435576	0.343680	0.024722	0.331947	-0.062711	0.056093	-0.007879	0.278408	0.326100	0.372405	0.441401	0.485558	0.227192	0.244300	-0.076275	0.039289	0.066942	0.003147	0.165227	0.021525	-0.007817
1stFlrSF	0.461239	0.456417	0.338733	0.243793	0.446875	0.470077	0.073346	0.275569	0.784538	1.000000	-0.298222	-0.011519	0.560631	0.278594	0.019899	0.365945	-0.088929	0.090188	0.084255	0.374373	0.404611	0.284615	0.441707	0.494192	0.219573	0.263813	-0.066071	0.028680	0.107902	0.112558	0.181387	0.048064	-0.013566
2ndFlrSF	-0.036185	-0.007862	0.025195	0.177177	0.063659	-0.188952	-0.095935	-0.006196	-0.238633	-0.298222	1.000000	-0.035688	0.618446	-0.153136	-0.095531	0.384515	0.613430	0.504458	0.079276	0.548464	0.143637	0.100355	0.181314	0.118704	0.087555	0.163780	0.048846	-0.047828	-0.018239	-0.006163	-0.022370	-0.009415	-0.010098
LowQualFinSF	-0.037294	-0.012457	-0.101154	-0.059973	-0.045886	-0.068178	-0.023976	0.067212	-0.013294	-0.011519	-0.035688	1.000000	0.050346	-0.046815	-0.020807	-0.004986	-0.053635	0.031993	-0.008344	0.065386	0.008156	-0.065324	-0.038844	-0.038496	-0.005262	-0.020197	0.115254	-0.007149	-0.013932	-0.004606	-0.007424	0.046473	0.026864
GrLivArea	0.357125	0.366324	0.290412	0.347946	0.416648	0.215692	-0.025137	0.226780	0.435576	0.560631	0.618446	0.050346	1.000000	0.088789	-0.069128	0.632701	0.453582	0.513831	0.137003	0.788012	0.456944	0.316098	0.515693	0.504555	0.255416	0.356366	-0.001413	-0.018561	0.071417	0.086542	0.128687	0.035472	-0.017434
BsmtFullBath	0.127314	0.094052	0.234922	0.150371	0.198270	0.628903	0.166500	-0.374659	0.343680	0.278594	-0.153136	-0.046815	0.088789	1.000000	-0.150071	0.025620	-0.035895	-0.159455	0.006600	-0.023118	0.201068	0.174664	0.189895	0.190121	0.196560	0.094315	-0.085258	0.068396	0.081740	0.014860	0.009305	0.018309	0.023824
BsmtHalfBath	-0.042779	-0.008378	-0.022947	-0.076928	0.003992	0.088971	0.123715	-0.117686	0.024722	0.019899	-0.095531	-0.020807	-0.069128	-0.150071	1.000000	-0.040176	-0.102009	-0.006723	-0.091839	-0.074981	0.049807	-0.041000	-0.044934	-0.018561	0.062275	-0.044065	-0.011173	0.018202	0.050842	0.127856	0.069801	0.015006	0.006073
FullBath	0.163078	0.147871	0.474028	0.477064	0.242522	0.104464	-0.074858	0.257721	0.331947	0.365945	0.384515	-0.004986	0.632701	0.025620	-0.040176	1.000000	0.180297	0.349285	0.210972	0.500354	0.228681	0.506458	0.489982	0.411278	0.175053	0.260829	-0.122930	-0.012760	-0.023736	0.000768	-0.006936	0.037308	0.010283
HalfBath	0.023850	0.079581	0.296700	0.238807	0.182094	-0.018966	-0.032627	-0.030584	-0.062711	-0.088929	0.613430	-0.053635	0.453582	-0.035895	-0.102009	0.180297	1.000000	0.263638	-0.015793	0.348590	0.207970	0.251409	0.249431	0.194229	0.125136	0.165246	-0.069873	-0.051598	-0.000446	-0.026345	0.046894	0.006309	0.013504
BedroomAbvGr	0.205100	0.181171	-0.035923	-0.004413	0.053259	-0.119743	-0.044951	0.199661	0.056093	0.090188	0.504458	0.031993	0.513831	-0.159455	-0.006723	0.349285	0.263638	1.000000	0.285674	0.664498	0.066133	-0.028440	0.099297	0.082304	0.016902	0.079231	0.057772	-0.085070	-0.028374	-0.007087	-0.005398	0.064727	-0.005113
KitchenAbvGr	0.016072	-0.031830	-0.098644	-0.135940	-0.066331	-0.092190	-0.034790	0.102069	-0.007879	0.084255	0.079276	-0.008344	0.137003	0.006600	-0.091839	0.210972	-0.015793	0.285674	1.000000	0.338219	-0.091652	-0.062182	-0.023325	-0.051080	-0.084779	-0.066172	0.018837	-0.018113	-0.061488	-0.011669	-0.005186	0.044159	0.038614
TotRmsAbvGrd	0.344366	0.289576	0.134839	0.203619	0.275533	0.060346	-0.060361	0.243566	0.278408	0.374373	0.548464	0.065386	0.788012	-0.023118	-0.074981	0.500354	0.348590	0.664498	0.338219	1.000000	0.294427	0.176934	0.355386	0.320217	0.146832	0.244571	0.027953	-0.059911	0.005290	0.055019	0.094063	0.050666	-0.031627
Fireplaces	0.257037	0.282210	0.193597	0.153965	0.301575	0.326075	0.083655	-0.043014	0.326100	0.404611	0.143637	0.008156	0.456944	0.201068	0.049807	0.228681	0.207970	0.066133	-0.091652	0.294427	1.000000	0.128770	0.341988	0.320092	0.254528	0.149040	0.025176	0.028696	0.156343	0.105926	0.014802	0.016598	0.010002
GarageYrBlt	0.082069	0.018330	0.844150	0.661765	0.257439	0.232798	-0.051545	0.153246	0.372405	0.284615	0.100355	-0.065324	0.316098	0.174664	-0.041000	0.506458	0.251409	-0.028440	-0.062182	0.176934	0.128770	1.000000	0.586649	0.548113	0.220850	0.235077	-0.303646	0.016753	-0.049821	-0.015421	0.007926	0.040189	-0.008451
GarageCars	0.336373	0.263398	0.538428	0.431442	0.358488	0.285959	0.005806	0.147005	0.441401	0.441707	0.181314	-0.038844	0.515693	0.189895	-0.044934	0.489982	0.249431	0.099297	-0.023325	0.355386	0.341988	0.586649	1.000000	0.896674	0.254332	0.194292	-0.116620	0.007189	0.036144	0.043302	0.002754	0.060845	-0.007032
GarageArea	0.375581	0.315841	0.482497	0.382034	0.375182	0.323800	0.022391	0.145625	0.485558	0.494192	0.118704	-0.038496	0.504555	0.190121	-0.018561	0.411278	0.194229	0.082304	-0.051080	0.320217	0.320092	0.548113	0.896674	1.000000	0.251051	0.224219	-0.092776	0.022661	0.073089	0.043922	0.036352	0.052470	0.000536
WoodDeckSF	0.157426	0.158483	0.233889	0.230724	0.172721	0.242369	0.126032	-0.073174	0.227192	0.219573	0.087555	-0.005262	0.255416	0.196560	0.062275	0.175053	0.125136	0.016902	-0.084779	0.146832	0.254528	0.220850	0.254332	0.251051	1.000000	0.019488	-0.113036	0.036622	-0.030682	0.123409	0.108898	0.014995	-0.022818
OpenPorchSF	0.179795	0.164815	0.208040	0.258049	0.163666	0.136321	-0.014185	0.111249	0.244300	0.263813	0.163780	-0.020197	0.356366	0.094315	-0.044065	0.260829	0.165246	0.079231	-0.066172	0.244571	0.149040	0.235077	0.194292	0.224219	0.019488	1.000000	-0.030918	-0.013865	0.022233	0.070795	0.150404	-0.000255	-0.017122
EnclosedPorch	0.013340	0.099850	-0.363012	-0.243582	-0.112814	-0.097441	0.029010	0.012468	-0.076275	-0.066071	0.048846	0.115254	-0.001413	-0.085258	-0.011173	-0.122930	-0.069873	0.057772	0.018837	0.027953	0.025176	-0.303646	-0.116620	-0.092776	-0.113036	-0.030918	1.000000	-0.027645	-0.048550	0.142589	0.001353	-0.012543	0.007616
3SsnPorch	-0.037487	-0.001846	-0.005442	0.025823	0.005772	0.088241	-0.014473	-0.046230	0.039289	0.028680	-0.047828	-0.007149	-0.018561	0.068396	0.018202	-0.012760	-0.051598	-0.085070	-0.018113	-0.059911	0.028696	0.016753	0.007189	0.022661	0.036622	-0.013865	-0.027645	1.000000	-0.026785	-0.005083	-0.001242	0.022444	0.027818
ScreenPorch	0.113444	0.088712	-0.031984	-0.053761	0.069339	0.131414	0.039806	-0.085111	0.066942	0.107902	-0.018239	-0.013932	0.071417	0.081740	0.050842	-0.023736	-0.000446	-0.028374	-0.061488	0.005290	0.156343	-0.049821	0.036144	0.073089	-0.030682	0.022233	-0.048550	-0.026785	1.000000	-0.004897	-0.012549	0.035212	-0.023439
PoolArea	0.134232	0.140494	-0.001060	-0.034862	-0.005395	0.012089	0.050152	-0.029672	0.003147	0.112558	-0.006163	-0.004606	0.086542	0.014860	0.127856	0.000768	-0.026345	-0.007087	-0.011669	0.055019	0.105926	-0.015421	0.043302	0.043922	0.123409	0.070795	0.142589	-0.005083	-0.004897	1.000000	-0.005279	-0.055731	-0.045185
MiscVal	0.068161	0.139071	0.007325	0.003011	0.105723	0.165403	-0.012808	0.000320	0.165227	0.181387	-0.022370	-0.007424	0.128687	0.009305	0.069801	-0.006936	0.046894	-0.005398	-0.005186	0.094063	0.014802	0.007926	0.002754	0.036352	0.108898	0.150404	0.001353	-0.001242	-0.012549	-0.005279	1.000000	0.019369	0.011829
MoSold	0.008810	0.005152	0.015599	0.011771	0.005118	0.013397	-0.003162	0.009132	0.021525	0.048064	-0.009415	0.046473	0.035472	0.018309	0.015006	0.037308	0.006309	0.064727	0.044159	0.050666	0.016598	0.040189	0.060845	0.052470	0.014995	-0.000255	-0.012543	0.022444	0.035212	-0.055731	0.019369	1.000000	-0.163924
YrSold	-0.025263	-0.051144	-0.011006	0.029715	-0.029556	0.030779	-0.011749	-0.035214	-0.007817	-0.013566	-0.010098	0.026864	-0.017434	0.023824	0.006073	0.010283	0.013504	-0.005113	0.038614	-0.031627	0.010002	-0.008451	-0.007032	0.000536	-0.022818	-0.017122	0.007616	0.027818	-0.023439	-0.045185	0.011829	-0.163924	1.000000

	LotFrontage	LotArea	YearBuilt	YearRemodAdd	MasVnrArea	BsmtFinSF1	BsmtFinSF2	BsmtUnfSF	TotalBsmtSF	1stFlrSF	2ndFlrSF	LowQualFinSF	GrLivArea	BedroomAbvGr	TotRmsAbvGrd	GarageYrBlt	GarageCars	GarageArea	WoodDeckSF	OpenPorchSF	EnclosedPorch	3SsnPorch	ScreenPorch	PoolArea	MiscVal	MoSold	YrSold
0	-0.561119	-0.150083	0.842112	0.534621	-0.570667	-0.301414	-0.28229	0.009986	-0.403595	-0.628924	-0.797828	-0.1294	-1.122539	-1.03986	-0.924643	0.824828	0.333696	0.518931	-0.749421	-0.685547	-0.360803	-0.112837	-0.271032	-0.069193	-0.108754	0.232125	-1.396198
1	-0.653665	-0.505027	-0.986602	-0.914670	-0.570667	-0.932224	-0.28229	0.361595	-0.714639	0.353300	-0.797828	-0.1294	-0.394941	0.14623	-0.319271	0.046080	-2.330655	-2.190237	1.284957	-0.685547	-0.360803	-0.112837	-0.271032	-0.069193	1.343344	-0.506915	1.618941
2	0.040432	-0.183080	-0.104901	-0.866360	-0.570667	0.850133	-0.28229	-1.121474	-0.311597	-0.633935	-0.797828	-0.1294	-1.126251	0.14623	-0.924643	-0.158854	-0.998480	-0.609889	-0.749421	-0.685547	-0.360803	-0.112837	-0.271032	-0.069193	-0.108754	-0.506915	1.618941
3	0.688257	-0.169171	-0.170212	-0.962979	-0.009994	0.428887	-0.28229	-0.276260	0.073922	0.398402	-0.797828	-0.1294	-0.361531	0.14623	-0.319271	-0.527734	0.333696	-0.120733	-0.749421	1.434220	-0.360803	-0.112837	-0.271032	-0.069193	-0.108754	2.079725	-1.396198
4	-0.237207	-0.361669	1.201324	1.066028	0.028864	0.473340	-0.28229	-0.053123	0.336776	0.107744	-0.797828	-0.1294	-0.576840	-1.03986	-0.319271	1.193708	0.333696	0.763509	0.062783	0.143927	-0.360803	-0.112837	-0.271032	-0.069193	-0.108754	0.971165	-0.642413

	LotFrontage	LotArea	YearBuilt	YearRemodAdd	MasVnrArea	BsmtFinSF1	BsmtFinSF2	BsmtUnfSF	TotalBsmtSF	1stFlrSF	2ndFlrSF	LowQualFinSF	GrLivArea	BedroomAbvGr	TotRmsAbvGrd	GarageYrBlt	GarageCars	GarageArea	WoodDeckSF	OpenPorchSF	EnclosedPorch	3SsnPorch	ScreenPorch	PoolArea	MiscVal	MoSold	YrSold
0	-0.052114	-0.083176	1.005390	0.776169	0.428552	-0.932224	-0.28229	0.713204	-0.372929	-0.704094	1.847518	-0.1294	0.958169	0.146230	0.891472	0.947788	0.333696	-0.148954	0.364459	0.051763	-0.360803	-0.112837	-0.271032	-0.069193	-0.108754	-0.506915	1.618941
1	-0.422299	-0.073531	-2.292826	-1.687625	-0.570667	-0.932224	-0.28229	1.188778	0.089255	-0.175397	0.762719	-0.1294	0.473722	1.332321	0.891472	-2.372137	1.665871	1.473725	-0.749421	-0.685547	1.088712	-0.112837	-0.271032	-0.069193	-0.108754	-0.506915	-1.396198
2	-1.717949	-0.706764	1.103357	0.921098	0.078825	-0.932224	-0.28229	0.920563	-0.171408	-0.473572	0.884529	-0.1294	0.351219	0.146230	0.286100	1.070748	0.333696	-0.402938	-0.749421	-0.071122	-0.360803	-0.112837	-0.271032	-0.069193	-0.108754	-0.876435	-1.396198
3	0.271798	-0.058301	0.907423	0.631240	-0.570667	1.131669	-0.28229	-0.979478	0.117731	-0.142823	1.475193	-0.1294	1.073249	0.146230	0.891472	0.824828	0.333696	0.551855	1.207604	1.925761	-0.360803	-0.112837	-0.271032	-0.069193	-0.108754	-0.137395	-1.396198
4	-0.422299	0.061502	-0.823324	-1.687625	-0.570667	0.295527	-0.28229	-0.528697	-0.309406	-0.293164	0.374306	-0.1294	0.072801	0.146230	-0.319271	-0.650694	-0.998480	-0.835653	-0.749421	-0.685547	-0.360803	-0.112837	-0.271032	-0.069193	-0.108754	-0.876435	0.111371

	Id	MSSubClass	MSZoning	LotFrontage	LotArea	Street	Alley	LotShape	LandContour	Utilities	LotConfig	LandSlope	Neighborhood	Condition1	Condition2	BldgType	HouseStyle	OverallQual	OverallCond	YearBuilt	YearRemodAdd	RoofStyle	RoofMatl	Exterior1st	Exterior2nd	MasVnrType	MasVnrArea	ExterQual	ExterCond	Foundation	BsmtQual	BsmtCond	BsmtExposure	BsmtFinType1	BsmtFinSF1	BsmtFinType2	BsmtUnfSF	TotalBsmtSF	Heating	HeatingQC	CentralAir	Electrical	1stFlrSF	2ndFlrSF	GrLivArea	BsmtFullBath	BsmtHalfBath	FullBath	HalfBath	BedroomAbvGr	KitchenAbvGr	KitchenQual	TotRmsAbvGrd	Functional	Fireplaces	FireplaceQu	GarageType	GarageYrBlt	GarageFinish	GarageCars	GarageArea	GarageQual	GarageCond	PavedDrive	WoodDeckSF	OpenPorchSF	EnclosedPorch	PoolQC	Fence	MiscFeature	MoSold	YrSold	SaleType	SaleCondition	SalePrice
0	1	60	RL	65.0	8450	Pave	NaN	Reg	Lvl	AllPub	Inside	Gtl	CollgCr	Norm	Norm	1Fam	2Story	7	5	2003	2003	Gable	CompShg	VinylSd	VinylSd	BrkFace	196.0	Gd	TA	PConc	Gd	TA	No	GLQ	706	Unf	150	856	GasA	Ex	Y	SBrkr	856	854	1710	1	0	2	1	3	1	Gd	8	Typ	0	NaN	Attchd	2003.0	RFn	2	548	TA	TA	Y	0	61	0	NaN	NaN	NaN	2	2008	WD	Normal	208500
1	2	20	RL	80.0	9600	Pave	NaN	Reg	Lvl	AllPub	FR2	Gtl	Veenker	Feedr	Norm	1Fam	1Story	6	8	1976	1976	Gable	CompShg	MetalSd	MetalSd	None	0.0	TA	TA	CBlock	Gd	TA	Gd	ALQ	978	Unf	284	1262	GasA	Ex	Y	SBrkr	1262	0	1262	0	1	2	0	3	1	TA	6	Typ	1	TA	Attchd	1976.0	RFn	2	460	TA	TA	Y	298	0	0	NaN	NaN	NaN	5	2007	WD	Normal	181500
2	3	60	RL	68.0	11250	Pave	NaN	IR1	Lvl	AllPub	Inside	Gtl	CollgCr	Norm	Norm	1Fam	2Story	7	5	2001	2002	Gable	CompShg	VinylSd	VinylSd	BrkFace	162.0	Gd	TA	PConc	Gd	TA	Mn	GLQ	486	Unf	434	920	GasA	Ex	Y	SBrkr	920	866	1786	1	0	2	1	3	1	Gd	6	Typ	1	TA	Attchd	2001.0	RFn	2	608	TA	TA	Y	0	42	0	NaN	NaN	NaN	9	2008	WD	Normal	223500
3	4	70	RL	60.0	9550	Pave	NaN	IR1	Lvl	AllPub	Corner	Gtl	Crawfor	Norm	Norm	1Fam	2Story	7	5	1915	1970	Gable	CompShg	Wd Sdng	Wd Shng	None	0.0	TA	TA	BrkTil	TA	Gd	No	ALQ	216	Unf	540	756	GasA	Gd	Y	SBrkr	961	756	1717	1	0	1	0	3	1	Gd	7	Typ	1	Gd	Detchd	1998.0	Unf	3	642	TA	TA	Y	0	35	272	NaN	NaN	NaN	2	2006	WD	Abnorml	140000
4	5	60	RL	84.0	14260	Pave	NaN	IR1	Lvl	AllPub	FR2	Gtl	NoRidge	Norm	Norm	1Fam	2Story	8	5	2000	2000	Gable	CompShg	VinylSd	VinylSd	BrkFace	350.0	Gd	TA	PConc	Gd	TA	Av	GLQ	655	Unf	490	1145	GasA	Ex	Y	SBrkr	1145	1053	2198	1	0	2	1	4	1	Gd	9	Typ	1	TA	Attchd	2000.0	RFn	3	836	TA	TA	Y	192	84	0	NaN	NaN	NaN	12	2008	WD	Normal	250000

	Id	MSSubClass	MSZoning	LotFrontage	LotArea	Street	Alley	LotShape	LandContour	Utilities	LotConfig	LandSlope	Neighborhood	Condition1	Condition2	BldgType	HouseStyle	OverallQual	OverallCond	YearBuilt	YearRemodAdd	RoofStyle	RoofMatl	Exterior1st	Exterior2nd	MasVnrType	MasVnrArea	ExterQual	ExterCond	Foundation	BsmtQual	BsmtCond	BsmtExposure	BsmtFinType1	BsmtFinSF1	BsmtFinType2	BsmtFinSF2	BsmtUnfSF	TotalBsmtSF	Heating	HeatingQC	CentralAir	Electrical	1stFlrSF	2ndFlrSF	GrLivArea	FullBath	HalfBath	BedroomAbvGr	KitchenAbvGr	KitchenQual	TotRmsAbvGrd	Functional	Fireplaces	FireplaceQu	GarageType	GarageYrBlt	GarageFinish	GarageCars	GarageArea	GarageQual	GarageCond	PavedDrive	WoodDeckSF	OpenPorchSF	ScreenPorch	PoolQC	Fence	MiscFeature	MiscVal	MoSold	YrSold	SaleType	SaleCondition
0	1461	20	RH	80.0	11622	Pave	NaN	Reg	Lvl	AllPub	Inside	Gtl	NAmes	Feedr	Norm	1Fam	1Story	5	6	1961	1961	Gable	CompShg	VinylSd	VinylSd	None	0.0	TA	TA	CBlock	TA	TA	No	Rec	468.0	LwQ	144.0	270.0	882.0	GasA	TA	Y	SBrkr	896	0	896	1	0	2	1	TA	5	Typ	0	NaN	Attchd	1961.0	Unf	1.0	730.0	TA	TA	Y	140	0	120	NaN	MnPrv	NaN	0	6	2010	WD	Normal
1	1462	20	RL	81.0	14267	Pave	NaN	IR1	Lvl	AllPub	Corner	Gtl	NAmes	Norm	Norm	1Fam	1Story	6	6	1958	1958	Hip	CompShg	Wd Sdng	Wd Sdng	BrkFace	108.0	TA	TA	CBlock	TA	TA	No	ALQ	923.0	Unf	0.0	406.0	1329.0	GasA	TA	Y	SBrkr	1329	0	1329	1	1	3	1	Gd	6	Typ	0	NaN	Attchd	1958.0	Unf	1.0	312.0	TA	TA	Y	393	36	0	NaN	NaN	Gar2	12500	6	2010	WD	Normal
2	1463	60	RL	74.0	13830	Pave	NaN	IR1	Lvl	AllPub	Inside	Gtl	Gilbert	Norm	Norm	1Fam	2Story	5	5	1997	1998	Gable	CompShg	VinylSd	VinylSd	None	0.0	TA	TA	PConc	Gd	TA	No	GLQ	791.0	Unf	0.0	137.0	928.0	GasA	Gd	Y	SBrkr	928	701	1629	2	1	3	1	TA	6	Typ	1	TA	Attchd	1997.0	Fin	2.0	482.0	TA	TA	Y	212	34	0	NaN	MnPrv	NaN	0	3	2010	WD	Normal
3	1464	60	RL	78.0	9978	Pave	NaN	IR1	Lvl	AllPub	Inside	Gtl	Gilbert	Norm	Norm	1Fam	2Story	6	6	1998	1998	Gable	CompShg	VinylSd	VinylSd	BrkFace	20.0	TA	TA	PConc	TA	TA	No	GLQ	602.0	Unf	0.0	324.0	926.0	GasA	Ex	Y	SBrkr	926	678	1604	2	1	3	1	Gd	7	Typ	1	Gd	Attchd	1998.0	Fin	2.0	470.0	TA	TA	Y	360	36	0	NaN	NaN	NaN	0	6	2010	WD	Normal
4	1465	120	RL	43.0	5005	Pave	NaN	IR1	HLS	AllPub	Inside	Gtl	StoneBr	Norm	Norm	TwnhsE	1Story	8	5	1992	1992	Gable	CompShg	HdBoard	HdBoard	None	0.0	Gd	TA	PConc	Gd	TA	No	ALQ	263.0	Unf	0.0	1017.0	1280.0	GasA	Ex	Y	SBrkr	1280	0	1280	2	0	2	1	Gd	5	Typ	0	NaN	Attchd	1992.0	RFn	2.0	506.0	TA	TA	Y	0	82	144	NaN	NaN	NaN	0	1	2010	WD	Normal

	Id	MSSubClass	MSZoning	LotFrontage	LotArea	Street	Alley	LotShape	LandContour	Utilities	LotConfig	LandSlope	Neighborhood	Condition1	Condition2	BldgType	HouseStyle	OverallQual	OverallCond	YearBuilt	YearRemodAdd	RoofStyle	RoofMatl	Exterior1st	Exterior2nd	MasVnrType	MasVnrArea	ExterQual	ExterCond	Foundation	BsmtQual	BsmtCond	BsmtExposure	BsmtFinType1	BsmtFinSF1	BsmtFinType2	BsmtFinSF2	BsmtUnfSF	TotalBsmtSF	Heating	HeatingQC	CentralAir	Electrical	1stFlrSF	2ndFlrSF	LowQualFinSF	GrLivArea	BsmtFullBath	BsmtHalfBath	FullBath	HalfBath	BedroomAbvGr	KitchenAbvGr	KitchenQual	TotRmsAbvGrd	Functional	Fireplaces	FireplaceQu	GarageType	GarageYrBlt	GarageFinish	GarageCars	GarageArea	GarageQual	GarageCond	PavedDrive	WoodDeckSF	OpenPorchSF	EnclosedPorch	3SsnPorch	ScreenPorch	PoolArea	PoolQC	Fence	MiscFeature	MiscVal	MoSold	YrSold	SaleType	SaleCondition	SalePrice
count	1460.000000	1460.000000	1460	1201.000000	1460.000000	1460	91	1460	1460	1460	1460	1460	1460	1460	1460	1460	1460	1460.000000	1460.000000	1460.000000	1460.000000	1460	1460	1460	1460	1452	1452.000000	1460	1460	1460	1423	1423	1422	1423	1460.000000	1422	1460.000000	1460.000000	1460.000000	1460	1460	1460	1459	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460	1460.000000	1460	1460.000000	770	1379	1379.000000	1379	1460.000000	1460.000000	1379	1379	1460	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	7	281	54	1460.000000	1460.000000	1460.000000	1460	1460	1460.000000
unique	NaN	NaN	5	NaN	NaN	2	2	4	4	2	5	3	25	9	8	5	8	NaN	NaN	NaN	NaN	6	8	15	16	4	NaN	4	5	6	4	4	4	6	NaN	6	NaN	NaN	NaN	6	5	2	5	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	4	NaN	7	NaN	5	6	NaN	3	NaN	NaN	5	5	3	NaN	NaN	NaN	NaN	NaN	NaN	3	4	4	NaN	NaN	NaN	9	6	NaN
top	NaN	NaN	RL	NaN	NaN	Pave	Grvl	Reg	Lvl	AllPub	Inside	Gtl	NAmes	Norm	Norm	1Fam	1Story	NaN	NaN	NaN	NaN	Gable	CompShg	VinylSd	VinylSd	None	NaN	TA	TA	PConc	TA	TA	No	Unf	NaN	Unf	NaN	NaN	NaN	GasA	Ex	Y	SBrkr	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	TA	NaN	Typ	NaN	Gd	Attchd	NaN	Unf	NaN	NaN	TA	TA	Y	NaN	NaN	NaN	NaN	NaN	NaN	Gd	MnPrv	Shed	NaN	NaN	NaN	WD	Normal	NaN
freq	NaN	NaN	1151	NaN	NaN	1454	50	925	1311	1459	1052	1382	225	1260	1445	1220	726	NaN	NaN	NaN	NaN	1141	1434	515	504	864	NaN	906	1282	647	649	1311	953	430	NaN	1256	NaN	NaN	NaN	1428	741	1365	1334	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	735	NaN	1360	NaN	380	870	NaN	605	NaN	NaN	1311	1326	1340	NaN	NaN	NaN	NaN	NaN	NaN	3	157	49	NaN	NaN	NaN	1267	1198	NaN
mean	730.500000	56.897260	NaN	70.049958	10516.828082	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	6.099315	5.575342	1971.267808	1984.865753	NaN	NaN	NaN	NaN	NaN	103.685262	NaN	NaN	NaN	NaN	NaN	NaN	NaN	443.639726	NaN	46.549315	567.240411	1057.429452	NaN	NaN	NaN	NaN	1162.626712	346.992466	5.844521	1515.463699	0.425342	0.057534	1.565068	0.382877	2.866438	1.046575	NaN	6.517808	NaN	0.613014	NaN	NaN	1978.506164	NaN	1.767123	472.980137	NaN	NaN	NaN	94.244521	46.660274	21.954110	3.409589	15.060959	2.758904	NaN	NaN	NaN	43.489041	6.321918	2007.815753	NaN	NaN	180921.195890
std	421.610009	42.300571	NaN	24.284752	9981.264932	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1.382997	1.112799	30.202904	20.645407	NaN	NaN	NaN	NaN	NaN	181.066207	NaN	NaN	NaN	NaN	NaN	NaN	NaN	456.098091	NaN	161.319273	441.866955	438.705324	NaN	NaN	NaN	NaN	386.587738	436.528436	48.623081	525.480383	0.518911	0.238753	0.550916	0.502885	0.815778	0.220338	NaN	1.625393	NaN	0.644666	NaN	NaN	24.689725	NaN	0.747315	213.804841	NaN	NaN	NaN	125.338794	66.256028	61.119149	29.317331	55.757415	40.177307	NaN	NaN	NaN	496.123024	2.703626	1.328095	NaN	NaN	79442.502883
min	1.000000	20.000000	NaN	21.000000	1300.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1.000000	1.000000	1872.000000	1950.000000	NaN	NaN	NaN	NaN	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.000000	NaN	0.000000	0.000000	0.000000	NaN	NaN	NaN	NaN	334.000000	0.000000	0.000000	334.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	NaN	2.000000	NaN	0.000000	NaN	NaN	1900.000000	NaN	0.000000	0.000000	NaN	NaN	NaN	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	NaN	NaN	NaN	0.000000	1.000000	2006.000000	NaN	NaN	34900.000000
25%	365.750000	20.000000	NaN	59.000000	7553.500000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	5.000000	5.000000	1954.000000	1967.000000	NaN	NaN	NaN	NaN	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.000000	NaN	0.000000	223.000000	795.750000	NaN	NaN	NaN	NaN	882.000000	0.000000	0.000000	1129.500000	0.000000	0.000000	1.000000	0.000000	2.000000	1.000000	NaN	5.000000	NaN	0.000000	NaN	NaN	1961.000000	NaN	1.000000	334.500000	NaN	NaN	NaN	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	NaN	NaN	NaN	0.000000	5.000000	2007.000000	NaN	NaN	129975.000000
50%	730.500000	50.000000	NaN	69.000000	9478.500000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	6.000000	5.000000	1973.000000	1994.000000	NaN	NaN	NaN	NaN	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	383.500000	NaN	0.000000	477.500000	991.500000	NaN	NaN	NaN	NaN	1087.000000	0.000000	0.000000	1464.000000	0.000000	0.000000	2.000000	0.000000	3.000000	1.000000	NaN	6.000000	NaN	1.000000	NaN	NaN	1980.000000	NaN	2.000000	480.000000	NaN	NaN	NaN	0.000000	25.000000	0.000000	0.000000	0.000000	0.000000	NaN	NaN	NaN	0.000000	6.000000	2008.000000	NaN	NaN	163000.000000
75%	1095.250000	70.000000	NaN	80.000000	11601.500000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	7.000000	6.000000	2000.000000	2004.000000	NaN	NaN	NaN	NaN	NaN	166.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	712.250000	NaN	0.000000	808.000000	1298.250000	NaN	NaN	NaN	NaN	1391.250000	728.000000	0.000000	1776.750000	1.000000	0.000000	2.000000	1.000000	3.000000	1.000000	NaN	7.000000	NaN	1.000000	NaN	NaN	2002.000000	NaN	2.000000	576.000000	NaN	NaN	NaN	168.000000	68.000000	0.000000	0.000000	0.000000	0.000000	NaN	NaN	NaN	0.000000	8.000000	2009.000000	NaN	NaN	214000.000000
max	1460.000000	190.000000	NaN	313.000000	215245.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	10.000000	9.000000	2010.000000	2010.000000	NaN	NaN	NaN	NaN	NaN	1600.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	5644.000000	NaN	1474.000000	2336.000000	6110.000000	NaN	NaN	NaN	NaN	4692.000000	2065.000000	572.000000	5642.000000	3.000000	2.000000	3.000000	2.000000	8.000000	3.000000	NaN	14.000000	NaN	3.000000	NaN	NaN	2010.000000	NaN	4.000000	1418.000000	NaN	NaN	NaN	857.000000	547.000000	552.000000	508.000000	480.000000	738.000000	NaN	NaN	NaN	15500.000000	12.000000	2010.000000	NaN	NaN	755000.000000

	Id	MSSubClass	MSZoning	LotFrontage	LotArea	Street	Alley	LotShape	LandContour	Utilities	LotConfig	LandSlope	Neighborhood	Condition1	Condition2	BldgType	HouseStyle	OverallQual	OverallCond	YearBuilt	YearRemodAdd	RoofStyle	RoofMatl	Exterior1st	Exterior2nd	MasVnrType	MasVnrArea	ExterQual	ExterCond	Foundation	BsmtQual	BsmtCond	BsmtExposure	BsmtFinType1	BsmtFinSF1	BsmtFinType2	BsmtFinSF2	BsmtUnfSF	TotalBsmtSF	Heating	HeatingQC	CentralAir	Electrical	1stFlrSF	2ndFlrSF	LowQualFinSF	GrLivArea	BsmtFullBath	BsmtHalfBath	FullBath	HalfBath	BedroomAbvGr	KitchenAbvGr	KitchenQual	TotRmsAbvGrd	Functional	Fireplaces	FireplaceQu	GarageType	GarageYrBlt	GarageFinish	GarageCars	GarageArea	GarageQual	GarageCond	PavedDrive	WoodDeckSF	OpenPorchSF	EnclosedPorch	3SsnPorch	ScreenPorch	PoolArea	PoolQC	Fence	MiscFeature	MiscVal	MoSold	YrSold	SaleType	SaleCondition
count	1459.000000	1459.000000	1455	1232.000000	1459.000000	1459	107	1459	1459	1457	1459	1459	1459	1459	1459	1459	1459	1459.000000	1459.000000	1459.000000	1459.000000	1459	1459	1458	1458	1443	1444.000000	1459	1459	1459	1415	1414	1415	1417	1458.000000	1417	1458.000000	1458.000000	1458.000000	1459	1459	1459	1459	1459.000000	1459.000000	1459.000000	1459.000000	1457.000000	1457.000000	1459.000000	1459.000000	1459.000000	1459.000000	1458	1459.000000	1457	1459.00000	729	1383	1381.000000	1381	1458.000000	1458.000000	1381	1381	1459	1459.000000	1459.000000	1459.000000	1459.000000	1459.000000	1459.000000	3	290	51	1459.000000	1459.000000	1459.000000	1458	1459
unique	NaN	NaN	5	NaN	NaN	2	2	4	4	1	5	3	25	9	5	5	7	NaN	NaN	NaN	NaN	6	4	13	15	4	NaN	4	5	6	4	4	4	6	NaN	6	NaN	NaN	NaN	4	5	2	4	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	4	NaN	7	NaN	5	6	NaN	3	NaN	NaN	4	5	3	NaN	NaN	NaN	NaN	NaN	NaN	2	4	3	NaN	NaN	NaN	9	6
top	NaN	NaN	RL	NaN	NaN	Pave	Grvl	Reg	Lvl	AllPub	Inside	Gtl	NAmes	Norm	Norm	1Fam	1Story	NaN	NaN	NaN	NaN	Gable	CompShg	VinylSd	VinylSd	None	NaN	TA	TA	PConc	TA	TA	No	GLQ	NaN	Unf	NaN	NaN	NaN	GasA	Ex	Y	SBrkr	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	TA	NaN	Typ	NaN	Gd	Attchd	NaN	Unf	NaN	NaN	TA	TA	Y	NaN	NaN	NaN	NaN	NaN	NaN	Ex	MnPrv	Shed	NaN	NaN	NaN	WD	Normal
freq	NaN	NaN	1114	NaN	NaN	1453	70	934	1311	1457	1081	1396	218	1251	1444	1205	745	NaN	NaN	NaN	NaN	1169	1442	510	510	878	NaN	892	1256	661	634	1295	951	431	NaN	1237	NaN	NaN	NaN	1446	752	1358	1337	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	757	NaN	1357	NaN	364	853	NaN	625	NaN	NaN	1293	1328	1301	NaN	NaN	NaN	NaN	NaN	NaN	2	172	46	NaN	NaN	NaN	1258	1204
mean	2190.000000	57.378341	NaN	68.580357	9819.161069	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	6.078821	5.553804	1971.357779	1983.662783	NaN	NaN	NaN	NaN	NaN	100.709141	NaN	NaN	NaN	NaN	NaN	NaN	NaN	439.203704	NaN	52.619342	554.294925	1046.117970	NaN	NaN	NaN	NaN	1156.534613	325.967786	3.543523	1486.045922	0.434454	0.065202	1.570939	0.377656	2.854010	1.042495	NaN	6.385195	NaN	0.58122	NaN	NaN	1977.721217	NaN	1.766118	472.768861	NaN	NaN	NaN	93.174777	48.313914	24.243317	1.794380	17.064428	1.744345	NaN	NaN	NaN	58.167923	6.104181	2007.769705	NaN	NaN
std	421.321334	42.746880	NaN	22.376841	4955.517327	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1.436812	1.113740	30.390071	21.130467	NaN	NaN	NaN	NaN	NaN	177.625900	NaN	NaN	NaN	NaN	NaN	NaN	NaN	455.268042	NaN	176.753926	437.260486	442.898624	NaN	NaN	NaN	NaN	398.165820	420.610226	44.043251	485.566099	0.530648	0.252468	0.555190	0.503017	0.829788	0.208472	NaN	1.508895	NaN	0.64742	NaN	NaN	26.431175	NaN	0.775945	217.048611	NaN	NaN	NaN	127.744882	68.883364	67.227765	20.207842	56.609763	30.491646	NaN	NaN	NaN	630.806978	2.722432	1.301740	NaN	NaN
min	1461.000000	20.000000	NaN	21.000000	1470.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1.000000	1.000000	1879.000000	1950.000000	NaN	NaN	NaN	NaN	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.000000	NaN	0.000000	0.000000	0.000000	NaN	NaN	NaN	NaN	407.000000	0.000000	0.000000	407.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	NaN	3.000000	NaN	0.00000	NaN	NaN	1895.000000	NaN	0.000000	0.000000	NaN	NaN	NaN	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	NaN	NaN	NaN	0.000000	1.000000	2006.000000	NaN	NaN
25%	1825.500000	20.000000	NaN	58.000000	7391.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	5.000000	5.000000	1953.000000	1963.000000	NaN	NaN	NaN	NaN	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.000000	NaN	0.000000	219.250000	784.000000	NaN	NaN	NaN	NaN	873.500000	0.000000	0.000000	1117.500000	0.000000	0.000000	1.000000	0.000000	2.000000	1.000000	NaN	5.000000	NaN	0.00000	NaN	NaN	1959.000000	NaN	1.000000	318.000000	NaN	NaN	NaN	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	NaN	NaN	NaN	0.000000	4.000000	2007.000000	NaN	NaN
50%	2190.000000	50.000000	NaN	67.000000	9399.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	6.000000	5.000000	1973.000000	1992.000000	NaN	NaN	NaN	NaN	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	350.500000	NaN	0.000000	460.000000	988.000000	NaN	NaN	NaN	NaN	1079.000000	0.000000	0.000000	1432.000000	0.000000	0.000000	2.000000	0.000000	3.000000	1.000000	NaN	6.000000	NaN	0.00000	NaN	NaN	1979.000000	NaN	2.000000	480.000000	NaN	NaN	NaN	0.000000	28.000000	0.000000	0.000000	0.000000	0.000000	NaN	NaN	NaN	0.000000	6.000000	2008.000000	NaN	NaN
75%	2554.500000	70.000000	NaN	80.000000	11517.500000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	7.000000	6.000000	2001.000000	2004.000000	NaN	NaN	NaN	NaN	NaN	164.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	753.500000	NaN	0.000000	797.750000	1305.000000	NaN	NaN	NaN	NaN	1382.500000	676.000000	0.000000	1721.000000	1.000000	0.000000	2.000000	1.000000	3.000000	1.000000	NaN	7.000000	NaN	1.00000	NaN	NaN	2002.000000	NaN	2.000000	576.000000	NaN	NaN	NaN	168.000000	72.000000	0.000000	0.000000	0.000000	0.000000	NaN	NaN	NaN	0.000000	8.000000	2009.000000	NaN	NaN
max	2919.000000	190.000000	NaN	200.000000	56600.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	10.000000	9.000000	2010.000000	2010.000000	NaN	NaN	NaN	NaN	NaN	1290.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	4010.000000	NaN	1526.000000	2140.000000	5095.000000	NaN	NaN	NaN	NaN	5095.000000	1862.000000	1064.000000	5095.000000	3.000000	2.000000	4.000000	2.000000	6.000000	2.000000	NaN	15.000000	NaN	4.00000	NaN	NaN	2207.000000	NaN	5.000000	1488.000000	NaN	NaN	NaN	1424.000000	742.000000	1012.000000	360.000000	576.000000	800.000000	NaN	NaN	NaN	17000.000000	12.000000	2010.000000	NaN	NaN

	dtypes	levels	null_values	Unique Values
Id	int64	[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...	0	1460
MSSubClass	int64	[60, 20, 70, 50, 190, 45, 90, 120, 30, 85, 80,...	0	15
MSZoning	object	[RL, RM, C (all), FV, RH]	0	5
LotFrontage	float64	[65.0, 80.0, 68.0, 60.0, 84.0, 85.0, 75.0, nan...	259	110
LotArea	int64	[8450, 9600, 11250, 9550, 14260, 14115, 10084,...	0	1073
Street	object	[Pave, Grvl]	0	2
Alley	object	[nan, Grvl, Pave]	1369	2
LotShape	object	[Reg, IR1, IR2, IR3]	0	4
LandContour	object	[Lvl, Bnk, Low, HLS]	0	4
Utilities	object	[AllPub, NoSeWa]	0	2
LotConfig	object	[Inside, FR2, Corner, CulDSac, FR3]	0	5
LandSlope	object	[Gtl, Mod, Sev]	0	3
Neighborhood	object	[CollgCr, Veenker, Crawfor, NoRidge, Mitchel, ...	0	25
Condition1	object	[Norm, Feedr, PosN, Artery, RRAe, RRNn, RRAn, ...	0	9
Condition2	object	[Norm, Artery, RRNn, Feedr, PosN, PosA, RRAn, ...	0	8
BldgType	object	[1Fam, 2fmCon, Duplex, TwnhsE, Twnhs]	0	5
HouseStyle	object	[2Story, 1Story, 1.5Fin, 1.5Unf, SFoyer, SLvl,...	0	8
OverallQual	int64	[7, 6, 8, 5, 9, 4, 10, 3, 1, 2]	0	10
OverallCond	int64	[5, 8, 6, 7, 4, 2, 3, 9, 1]	0	9
YearBuilt	int64	[2003, 1976, 2001, 1915, 2000, 1993, 2004, 197...	0	112
YearRemodAdd	int64	[2003, 1976, 2002, 1970, 2000, 1995, 2005, 197...	0	61
RoofStyle	object	[Gable, Hip, Gambrel, Mansard, Flat, Shed]	0	6
RoofMatl	object	[CompShg, WdShngl, Metal, WdShake, Membran, Ta...	0	8
Exterior1st	object	[VinylSd, MetalSd, Wd Sdng, HdBoard, BrkFace, ...	0	15
Exterior2nd	object	[VinylSd, MetalSd, Wd Shng, HdBoard, Plywood, ...	0	16
MasVnrType	object	[BrkFace, None, Stone, BrkCmn, nan]	8	4
MasVnrArea	float64	[196.0, 0.0, 162.0, 350.0, 186.0, 240.0, 286.0...	8	327
ExterQual	object	[Gd, TA, Ex, Fa]	0	4
ExterCond	object	[TA, Gd, Fa, Po, Ex]	0	5
Foundation	object	[PConc, CBlock, BrkTil, Wood, Slab, Stone]	0	6
BsmtQual	object	[Gd, TA, Ex, nan, Fa]	37	4
BsmtCond	object	[TA, Gd, nan, Fa, Po]	37	4
BsmtExposure	object	[No, Gd, Mn, Av, nan]	38	4
BsmtFinType1	object	[GLQ, ALQ, Unf, Rec, BLQ, nan, LwQ]	37	6
BsmtFinSF1	int64	[706, 978, 486, 216, 655, 732, 1369, 859, 0, 8...	0	637
BsmtFinType2	object	[Unf, BLQ, nan, ALQ, Rec, LwQ, GLQ]	38	6
BsmtFinSF2	int64	[0, 32, 668, 486, 93, 491, 506, 712, 362, 41, ...	0	144
BsmtUnfSF	int64	[150, 284, 434, 540, 490, 64, 317, 216, 952, 1...	0	780
TotalBsmtSF	int64	[856, 1262, 920, 756, 1145, 796, 1686, 1107, 9...	0	721
Heating	object	[GasA, GasW, Grav, Wall, OthW, Floor]	0	6
HeatingQC	object	[Ex, Gd, TA, Fa, Po]	0	5
CentralAir	object	[Y, N]	0	2
Electrical	object	[SBrkr, FuseF, FuseA, FuseP, Mix, nan]	1	5
1stFlrSF	int64	[856, 1262, 920, 961, 1145, 796, 1694, 1107, 1...	0	753
2ndFlrSF	int64	[854, 0, 866, 756, 1053, 566, 983, 752, 1142, ...	0	417
LowQualFinSF	int64	[0, 360, 513, 234, 528, 572, 144, 392, 371, 39...	0	24
GrLivArea	int64	[1710, 1262, 1786, 1717, 2198, 1362, 1694, 209...	0	861
BsmtFullBath	int64	[1, 0, 2, 3]	0	4
BsmtHalfBath	int64	[0, 1, 2]	0	3
FullBath	int64	[2, 1, 3, 0]	0	4
HalfBath	int64	[1, 0, 2]	0	3
BedroomAbvGr	int64	[3, 4, 1, 2, 0, 5, 6, 8]	0	8
KitchenAbvGr	int64	[1, 2, 3, 0]	0	4
KitchenQual	object	[Gd, TA, Ex, Fa]	0	4
TotRmsAbvGrd	int64	[8, 6, 7, 9, 5, 11, 4, 10, 12, 3, 2, 14]	0	12
Functional	object	[Typ, Min1, Maj1, Min2, Mod, Maj2, Sev]	0	7
Fireplaces	int64	[0, 1, 2, 3]	0	4
FireplaceQu	object	[nan, TA, Gd, Fa, Ex, Po]	690	5
GarageType	object	[Attchd, Detchd, BuiltIn, CarPort, nan, Basmen...	81	6
GarageYrBlt	float64	[2003.0, 1976.0, 2001.0, 1998.0, 2000.0, 1993....	81	97
GarageFinish	object	[RFn, Unf, Fin, nan]	81	3
GarageCars	int64	[2, 3, 1, 0, 4]	0	5
GarageArea	int64	[548, 460, 608, 642, 836, 480, 636, 484, 468, ...	0	441
GarageQual	object	[TA, Fa, Gd, nan, Ex, Po]	81	5
GarageCond	object	[TA, Fa, nan, Gd, Po, Ex]	81	5
PavedDrive	object	[Y, N, P]	0	3
WoodDeckSF	int64	[0, 298, 192, 40, 255, 235, 90, 147, 140, 160,...	0	274
OpenPorchSF	int64	[61, 0, 42, 35, 84, 30, 57, 204, 4, 21, 33, 21...	0	202
EnclosedPorch	int64	[0, 272, 228, 205, 176, 87, 172, 102, 37, 144,...	0	120
3SsnPorch	int64	[0, 320, 407, 130, 180, 168, 140, 508, 238, 24...	0	20
ScreenPorch	int64	[0, 176, 198, 291, 252, 99, 184, 168, 130, 142...	0	76
PoolArea	int64	[0, 512, 648, 576, 555, 480, 519, 738]	0	8
PoolQC	object	[nan, Ex, Fa, Gd]	1453	3
Fence	object	[nan, MnPrv, GdWo, GdPrv, MnWw]	1179	4
MiscFeature	object	[nan, Shed, Gar2, Othr, TenC]	1406	4
MiscVal	int64	[0, 700, 350, 500, 400, 480, 450, 15500, 1200,...	0	21
MoSold	int64	[2, 5, 9, 12, 10, 8, 11, 4, 1, 7, 3, 6]	0	12
YrSold	int64	[2008, 2007, 2006, 2009, 2010]	0	5
SaleType	object	[WD, New, COD, ConLD, ConLI, CWD, ConLw, Con, ...	0	9
SaleCondition	object	[Normal, Abnorml, Partial, AdjLand, Alloca, Fa...	0	6
SalePrice	int64	[208500, 181500, 223500, 140000, 250000, 14300...	0	663

	dtypes	levels	null_values	Unique Values
Id	int64	[1461, 1462, 1463, 1464, 1465, 1466, 1467, 146...	0	1459
MSSubClass	int64	[20, 60, 120, 160, 80, 30, 50, 90, 85, 190, 45...	0	16
MSZoning	object	[RH, RL, RM, FV, C (all), nan]	4	5
LotFrontage	float64	[80.0, 81.0, 74.0, 78.0, 43.0, 75.0, nan, 63.0...	227	115
LotArea	int64	[11622, 14267, 13830, 9978, 5005, 10000, 7980,...	0	1106
Street	object	[Pave, Grvl]	0	2
Alley	object	[nan, Pave, Grvl]	1352	2
LotShape	object	[Reg, IR1, IR2, IR3]	0	4
LandContour	object	[Lvl, HLS, Bnk, Low]	0	4
Utilities	object	[AllPub, nan]	2	1
LotConfig	object	[Inside, Corner, FR2, CulDSac, FR3]	0	5
LandSlope	object	[Gtl, Mod, Sev]	0	3
Neighborhood	object	[NAmes, Gilbert, StoneBr, BrDale, NPkVill, Nri...	0	25
Condition1	object	[Feedr, Norm, PosN, RRNe, Artery, RRNn, PosA, ...	0	9
Condition2	object	[Norm, Feedr, PosA, PosN, Artery]	0	5
BldgType	object	[1Fam, TwnhsE, Twnhs, Duplex, 2fmCon]	0	5
HouseStyle	object	[1Story, 2Story, SLvl, 1.5Fin, SFoyer, 2.5Unf,...	0	7
OverallQual	int64	[5, 6, 8, 7, 4, 9, 2, 3, 10, 1]	0	10
OverallCond	int64	[6, 5, 7, 8, 2, 9, 3, 4, 1]	0	9
YearBuilt	int64	[1961, 1958, 1997, 1998, 1992, 1993, 1990, 197...	0	106
YearRemodAdd	int64	[1961, 1958, 1998, 1992, 1994, 2007, 1990, 197...	0	61
RoofStyle	object	[Gable, Hip, Gambrel, Flat, Mansard, Shed]	0	6
RoofMatl	object	[CompShg, Tar&Grv, WdShake, WdShngl]	0	4
Exterior1st	object	[VinylSd, Wd Sdng, HdBoard, Plywood, MetalSd, ...	1	13
Exterior2nd	object	[VinylSd, Wd Sdng, HdBoard, Plywood, MetalSd, ...	1	15
MasVnrType	object	[None, BrkFace, Stone, BrkCmn, nan]	16	4
MasVnrArea	float64	[0.0, 108.0, 20.0, 504.0, 492.0, 162.0, 256.0,...	15	303
ExterQual	object	[TA, Gd, Ex, Fa]	0	4
ExterCond	object	[TA, Gd, Fa, Po, Ex]	0	5
Foundation	object	[CBlock, PConc, BrkTil, Stone, Slab, Wood]	0	6
BsmtQual	object	[TA, Gd, Ex, Fa, nan]	44	4
BsmtCond	object	[TA, Po, Fa, Gd, nan]	45	4
BsmtExposure	object	[No, Gd, Mn, Av, nan]	44	4
BsmtFinType1	object	[Rec, ALQ, GLQ, Unf, BLQ, LwQ, nan]	42	6
BsmtFinSF1	float64	[468.0, 923.0, 791.0, 602.0, 263.0, 0.0, 935.0...	1	669
BsmtFinType2	object	[LwQ, Unf, Rec, BLQ, GLQ, ALQ, nan]	42	6
BsmtFinSF2	float64	[144.0, 0.0, 78.0, 859.0, 981.0, 42.0, 46.0, 1...	1	161
BsmtUnfSF	float64	[270.0, 406.0, 137.0, 324.0, 1017.0, 763.0, 23...	1	793
TotalBsmtSF	float64	[882.0, 1329.0, 928.0, 926.0, 1280.0, 763.0, 1...	1	736
Heating	object	[GasA, GasW, Grav, Wall]	0	4
HeatingQC	object	[TA, Gd, Ex, Fa, Po]	0	5
CentralAir	object	[Y, N]	0	2
Electrical	object	[SBrkr, FuseA, FuseF, FuseP]	0	4
1stFlrSF	int64	[896, 1329, 928, 926, 1280, 763, 1187, 789, 13...	0	789
2ndFlrSF	int64	[0, 701, 678, 892, 676, 504, 567, 601, 707, 56...	0	407
LowQualFinSF	int64	[0, 362, 1064, 431, 436, 259, 312, 108, 697, 5...	0	15
GrLivArea	int64	[896, 1329, 1629, 1604, 1280, 1655, 1187, 1465...	0	879
BsmtFullBath	float64	[0.0, 1.0, 2.0, 3.0, nan]	2	4
BsmtHalfBath	float64	[0.0, 1.0, nan, 2.0]	2	3
FullBath	int64	[1, 2, 3, 4, 0]	0	5
HalfBath	int64	[0, 1, 2]	0	3
BedroomAbvGr	int64	[2, 3, 4, 1, 6, 5, 0]	0	7
KitchenAbvGr	int64	[1, 2, 0]	0	3
KitchenQual	object	[TA, Gd, Ex, Fa, nan]	1	4
TotRmsAbvGrd	int64	[5, 6, 7, 4, 10, 8, 9, 3, 12, 11, 13, 15]	0	12
Functional	object	[Typ, Min2, Min1, Mod, Maj1, Sev, Maj2, nan]	2	7
Fireplaces	int64	[0, 1, 2, 3, 4]	0	5
FireplaceQu	object	[nan, TA, Gd, Po, Fa, Ex]	730	5
GarageType	object	[Attchd, Detchd, BuiltIn, nan, Basment, 2Types...	76	6
GarageYrBlt	float64	[1961.0, 1958.0, 1997.0, 1998.0, 1992.0, 1993....	78	97
GarageFinish	object	[Unf, Fin, RFn, nan]	78	3
GarageCars	float64	[1.0, 2.0, 3.0, 0.0, 4.0, 5.0, nan]	1	6
GarageArea	float64	[730.0, 312.0, 482.0, 470.0, 506.0, 440.0, 420...	1	459
GarageQual	object	[TA, nan, Fa, Gd, Po]	78	4
GarageCond	object	[TA, nan, Fa, Gd, Po, Ex]	78	5
PavedDrive	object	[Y, N, P]	0	3
WoodDeckSF	int64	[140, 393, 212, 360, 0, 157, 483, 192, 240, 20...	0	263
OpenPorchSF	int64	[0, 36, 34, 82, 84, 21, 75, 68, 30, 133, 35, 7...	0	203
EnclosedPorch	int64	[0, 80, 186, 120, 150, 205, 113, 135, 126, 334...	0	131
3SsnPorch	int64	[0, 224, 255, 225, 360, 150, 153, 174, 120, 21...	0	13
ScreenPorch	int64	[120, 0, 144, 256, 216, 204, 160, 240, 148, 16...	0	75
PoolArea	int64	[0, 144, 368, 444, 228, 561, 800]	0	7
PoolQC	object	[nan, Ex, Gd]	1456	2
Fence	object	[MnPrv, nan, GdPrv, GdWo, MnWw]	1169	4
MiscFeature	object	[nan, Gar2, Shed, Othr]	1408	3
MiscVal	int64	[0, 12500, 500, 1500, 300, 450, 80, 600, 490, ...	0	26
MoSold	int64	[6, 3, 1, 4, 5, 2, 7, 10, 8, 11, 9, 12]	0	12
YrSold	int64	[2010, 2009, 2008, 2007, 2006]	0	5
SaleType	object	[WD, COD, New, ConLD, Oth, Con, ConLw, ConLI, ...	1	9
SaleCondition	object	[Normal, Partial, Abnorml, Family, Alloca, Adj...	0	6

	LotFrontage	LotArea	YearBuilt	YearRemodAdd	MasVnrArea	BsmtFinSF1	BsmtUnfSF	TotalBsmtSF	1stFlrSF	GrLivArea	BedroomAbvGr	TotRmsAbvGrd	GarageYrBlt	GarageCars	GarageArea	WoodDeckSF	OpenPorchSF	MiscVal	MoSold	YrSold
0	57.0	8846.0	1996.0	1996.0	0.0	298.0	572.0	870.0	914.0	914.0	2.0	5.0	1998.0	2.0	576.0	0.0	0.0	0.0	7.0	2006.0
1	55.0	5350.0	1940.0	1966.0	0.0	0.0	728.0	728.0	1306.0	1306.0	3.0	6.0	1979.0	0.0	0.0	263.0	0.0	450.0	5.0	2010.0
2	70.0	8521.0	1967.0	1967.0	0.0	842.0	70.0	912.0	912.0	912.0	3.0	5.0	1974.0	1.0	336.0	0.0	0.0	0.0	5.0	2010.0
3	84.0	8658.0	1965.0	1965.0	101.0	643.0	445.0	1088.0	1324.0	1324.0	3.0	6.0	1965.0	2.0	440.0	0.0	138.0	0.0	12.0	2006.0
4	64.0	6762.0	2007.0	2007.0	108.0	664.0	544.0	1208.0	1208.0	1208.0	2.0	6.0	2007.0	2.0	628.0	105.0	54.0	0.0	9.0	2007.0

	MSSubClass	MSZoning	Street	Alley	LotShape	LandContour	Utilities	LotConfig	LandSlope	Neighborhood	Condition1	Condition2	BldgType	HouseStyle	OverallQual	OverallCond	RoofStyle	RoofMatl	Exterior1st	Exterior2nd	MasVnrType	ExterQual	ExterCond	Foundation	BsmtQual	BsmtCond	BsmtExposure	BsmtFinType1	BsmtFinType2	Heating	HeatingQC	CentralAir	Electrical	KitchenQual	Functional	FireplaceQu	GarageType	GarageFinish	GarageQual	GarageCond	PavedDrive	PoolQC	Fence	MiscFeature	SaleType	SaleCondition
0	85	RL	Pave	NAA	IR1	Lvl	AllPub	CulDSac	Gtl	CollgCr	Norm	Norm	1Fam	SFoyer	5	5	Gable	CompShg	VinylSd	VinylSd	None	Gd	TA	PConc	Gd	TA	Av	GLQ	Unf	GasA	Ex	Y	SBrkr	TA	Typ	NF	Detchd	Unf	TA	TA	Y	NP	NF	NE	WD	Normal
1	30	RL	Pave	NAA	IR1	Lvl	AllPub	Inside	Gtl	BrkSide	Norm	Norm	1Fam	1Story	3	2	Gable	CompShg	Wd Sdng	Plywood	None	TA	Po	CBlock	TA	TA	No	Unf	Unf	GasA	Ex	Y	SBrkr	Fa	Mod	NF	NG	NG	NG	NG	Y	NP	GdWo	Shed	WD	Normal
2	20	RL	Pave	NAA	Reg	Lvl	AllPub	FR2	Gtl	Sawyer	Feedr	Norm	1Fam	1Story	5	5	Gable	CompShg	HdBoard	HdBoard	None	TA	TA	CBlock	TA	TA	No	ALQ	Unf	GasA	TA	Y	SBrkr	TA	Typ	Fa	Detchd	Unf	TA	TA	Y	NP	MnPrv	NE	WD	Normal
3	20	RL	Pave	NAA	Reg	Lvl	AllPub	Inside	Gtl	NAmes	Norm	Norm	1Fam	1Story	6	5	Gable	CompShg	Wd Sdng	Wd Sdng	BrkFace	TA	TA	CBlock	TA	TA	No	Rec	Unf	GasA	Ex	Y	SBrkr	TA	Typ	TA	Attchd	RFn	TA	TA	Y	NP	GdWo	NE	WD	Abnorml
4	20	RL	Pave	NAA	Reg	Lvl	AllPub	Inside	Gtl	CollgCr	Norm	Norm	1Fam	1Story	7	5	Gable	CompShg	VinylSd	VinylSd	BrkFace	Gd	TA	PConc	Gd	TA	No	GLQ	Unf	GasA	Ex	Y	SBrkr	Gd	Typ	NF	Attchd	RFn	TA	TA	Y	NP	NF	NE	New	Partial

	LotFrontage	LotArea	YearBuilt	YearRemodAdd	MasVnrArea	BsmtFinSF1	BsmtUnfSF	TotalBsmtSF	1stFlrSF	2ndFlrSF	GrLivArea	BedroomAbvGr	TotRmsAbvGrd	GarageYrBlt	GarageCars	GarageArea	WoodDeckSF	OpenPorchSF	EnclosedPorch	MoSold	YrSold
0	68.0	9505.0	2001.0	2001.0	180.0	0.0	884.0	884.0	884.0	1151.0	2035.0	3.0	8.0	2001.0	2.0	434.0	144.0	48.0	0.0	5.0	2010.0
1	60.0	9600.0	1900.0	1950.0	0.0	0.0	1095.0	1095.0	1095.0	679.0	1774.0	4.0	8.0	1920.0	3.0	779.0	0.0	0.0	90.0	5.0	2006.0
2	32.0	3363.0	2004.0	2004.0	117.0	0.0	976.0	976.0	976.0	732.0	1708.0	3.0	7.0	2004.0	2.0	380.0	0.0	40.0	0.0	4.0	2006.0
3	75.0	9750.0	1998.0	1998.0	0.0	975.0	133.0	1108.0	1108.0	989.0	2097.0	3.0	8.0	1998.0	2.0	583.0	253.0	170.0	0.0	6.0	2006.0
4	60.0	10930.0	1945.0	1950.0	0.0	580.0	333.0	913.0	1048.0	510.0	1558.0	3.0	6.0	1962.0	1.0	288.0	0.0	0.0	0.0	4.0	2008.0

	LotFrontage	LotArea	YearBuilt	YearRemodAdd	MasVnrArea	BsmtFinSF1	BsmtFinSF2	BsmtUnfSF	TotalBsmtSF	1stFlrSF	2ndFlrSF	GrLivArea	BedroomAbvGr	TotRmsAbvGrd	GarageYrBlt	GarageCars	GarageArea	WoodDeckSF	OpenPorchSF	ScreenPorch	MiscVal	MoSold	YrSold
0	80.0	11622.0	1961.0	1961.0	0.0	468.0	144.0	270.0	882.0	896.0	0.0	896.0	2.0	5.0	1961.0	1.0	730.0	140.0	0.0	120.0	0.0	6.0	2010.0
1	81.0	14267.0	1958.0	1958.0	108.0	923.0	0.0	406.0	1329.0	1329.0	0.0	1329.0	3.0	6.0	1958.0	1.0	312.0	393.0	36.0	0.0	12500.0	6.0	2010.0
2	74.0	13830.0	1997.0	1998.0	0.0	791.0	0.0	137.0	928.0	928.0	701.0	1629.0	3.0	6.0	1997.0	2.0	482.0	212.0	34.0	0.0	0.0	3.0	2010.0
3	78.0	9978.0	1998.0	1998.0	20.0	602.0	0.0	324.0	926.0	926.0	678.0	1604.0	3.0	7.0	1998.0	2.0	470.0	360.0	36.0	0.0	0.0	6.0	2010.0
4	43.0	5005.0	1992.0	1992.0	0.0	263.0	0.0	1017.0	1280.0	1280.0	0.0	1280.0	2.0	5.0	1992.0	2.0	506.0	0.0	82.0	144.0	0.0	1.0	2010.0

	MSSubClass_20	MSSubClass_30	MSSubClass_85	MSZoning_RL	Street_Pave	Alley_NAA	LotShape_Reg	LandContour_Lvl	LotConfig_CulDSac	LotConfig_FR2	LotConfig_Inside	Neighborhood_BrkSide	Neighborhood_CollgCr	Neighborhood_NAmes	Neighborhood_Sawyer	Condition1_Feedr	Condition1_Norm	Condition2_Norm	HouseStyle_1Story	HouseStyle_SFoyer	OverallQual_3	OverallQual_5	OverallQual_6	OverallQual_7	OverallCond_2	OverallCond_5	...	Foundation_CBlock	Foundation_PConc	BsmtQual_Gd	BsmtQual_TA	BsmtCond_TA	BsmtExposure_No	BsmtFinType1_GLQ	BsmtFinType1_Rec	BsmtFinType1_Unf	BsmtFinType2_Unf	HeatingQC_TA	CentralAir_Y	Electrical_SBrkr	KitchenQual_Fa	KitchenQual_Gd	KitchenQual_TA	Functional_Mod	Functional_Typ	FireplaceQu_Fa	FireplaceQu_NF	FireplaceQu_TA	GarageType_Attchd	GarageType_Detchd	GarageType_NG	GarageFinish_NG	GarageFinish_RFn	GarageFinish_Unf	GarageQual_NG	GarageQual_TA	GarageCond_NG	GarageCond_TA	PavedDrive_Y	PoolQC_NP	Fence_GdWo	Fence_MnPrv	Fence_NF	MiscFeature_NE	MiscFeature_Shed	SaleType_New	SaleType_WD	SaleCondition_Normal	SaleCondition_Partial
0	0	0	1	1	1	1	0	1	1	0	0	0	1	0	0	0	1	1	0	1	0	1	0	0	0	1	...	0	1	1	0	1	0	1	0	0	1	0	1	1	0	0	1	0	1	0	1	0	0	1	0	0	0	1	0	1	0	1	1	1	0	0	1	1	0	0	1	1	0
1	0	1	0	1	1	1	0	1	0	0	1	1	0	0	0	0	1	1	1	0	1	0	0	0	1	0	...	1	0	0	1	1	1	0	0	1	1	0	1	1	1	0	0	1	0	0	1	0	0	0	1	1	0	0	1	0	1	0	1	1	1	0	0	0	1	0	1	1	0
2	1	0	0	1	1	1	1	1	0	1	0	0	0	0	1	1	0	1	1	0	0	1	0	0	0	1	...	1	0	0	1	1	1	0	0	0	1	1	1	1	0	0	1	0	1	1	0	0	0	1	0	0	0	1	0	1	0	1	1	1	0	1	0	1	0	0	1	1	0
3	1	0	0	1	1	1	1	1	0	0	1	0	0	1	0	0	1	1	1	0	0	0	1	0	0	1	...	1	0	0	1	1	1	0	1	0	1	0	1	1	0	0	1	0	1	0	0	1	1	0	0	0	1	0	0	1	0	1	1	1	1	0	0	1	0	0	1	0	0
4	1	0	0	1	1	1	1	1	0	0	1	0	1	0	0	0	1	1	1	0	0	0	0	1	0	1	...	0	1	1	0	1	1	1	0	0	1	0	1	1	0	1	0	0	1	0	1	0	1	0	0	0	1	0	0	1	0	1	1	1	0	0	1	1	0	1	0	0	1

	MSSubClass_160	MSSubClass_50	MSSubClass_60	MSSubClass_70	MSZoning_RL	MSZoning_RM	Street_Pave	Alley_NAA	LotShape_Reg	LandContour_Lvl	LotConfig_CulDSac	LotConfig_Inside	Neighborhood_CollgCr	Neighborhood_Edwards	Neighborhood_Gilbert	Neighborhood_NAmes	Neighborhood_OldTown	Condition1_Norm	Condition2_Norm	BldgType_TwnhsE	HouseStyle_2Story	OverallQual_4	OverallQual_5	OverallQual_7	OverallCond_5	OverallCond_6	RoofStyle_Gable	...	ExterQual_Gd	ExterQual_TA	ExterCond_TA	Foundation_CBlock	Foundation_PConc	BsmtQual_Gd	BsmtQual_TA	BsmtCond_TA	BsmtExposure_No	BsmtFinType1_BLQ	BsmtFinType1_GLQ	BsmtFinType1_Unf	BsmtFinType2_Unf	Heating_GasA	Heating_GasW	HeatingQC_Fa	HeatingQC_TA	CentralAir_Y	Electrical_SBrkr	KitchenQual_Gd	KitchenQual_TA	Functional_Min2	Functional_Typ	FireplaceQu_Gd	FireplaceQu_NF	FireplaceQu_TA	GarageType_Attchd	GarageType_BuiltIn	GarageType_Detchd	GarageFinish_RFn	GarageFinish_Unf	GarageQual_Fa	GarageQual_TA	GarageCond_Fa	GarageCond_TA	PavedDrive_Y	PoolQC_NP	Fence_NF	MiscFeature_NE	SaleType_WD	SaleCondition_Normal
0	0	0	1	0	1	0	1	1	0	1	1	0	0	0	1	0	0	1	1	0	1	0	0	1	1	0	1	...	1	0	1	0	1	1	0	1	1	0	0	1	1	1	0	0	0	1	1	1	0	0	1	1	0	0	0	1	0	0	0	0	1	0	1	1	1	1	1	1	1
1	0	0	0	1	0	1	1	0	1	1	0	1	0	0	0	0	1	1	1	0	1	1	0	0	0	0	1	...	0	1	1	0	0	0	1	0	1	0	0	1	1	0	1	1	0	0	1	0	1	1	0	0	1	0	0	0	0	0	1	1	0	1	0	0	1	1	1	1	1
2	1	0	0	0	0	1	1	1	1	1	0	1	0	1	0	0	0	1	1	1	1	0	0	1	1	0	1	...	1	0	1	0	1	1	0	1	1	0	0	1	1	1	0	0	0	1	1	1	0	0	0	0	1	0	0	0	1	0	1	0	1	0	1	1	1	1	1	1	1
3	0	0	1	0	1	0	1	1	1	1	0	0	1	0	0	0	0	1	1	0	1	0	0	1	0	1	1	...	0	1	1	0	1	1	0	1	0	0	1	0	1	1	0	0	0	1	1	1	0	0	1	0	0	1	0	0	1	1	0	0	1	0	1	1	1	1	1	1	1
4	0	1	0	0	1	0	1	0	1	0	0	1	0	0	0	1	0	0	1	0	0	0	1	0	0	1	1	...	0	1	1	1	0	0	1	1	1	1	0	0	1	1	0	0	1	1	0	0	1	0	1	0	0	1	1	0	0	0	1	0	1	0	1	1	1	1	1	1	1

	LotFrontage	LotArea	YearBuilt	YearRemodAdd	MasVnrArea	BsmtFinSF1	BsmtFinSF2	BsmtUnfSF	TotalBsmtSF	1stFlrSF	2ndFlrSF	LowQualFinSF	GrLivArea	BedroomAbvGr	TotRmsAbvGrd	GarageYrBlt	GarageCars	GarageArea	WoodDeckSF	OpenPorchSF	EnclosedPorch	3SsnPorch	ScreenPorch	PoolArea	MiscVal	MoSold	YrSold	MSSubClass_20	MSSubClass_30	MSSubClass_85	MSZoning_RL	Street_Pave	Alley_NAA	LotShape_Reg	LandContour_Lvl	LotConfig_CulDSac	LotConfig_FR2	LotConfig_Inside	Neighborhood_BrkSide	Neighborhood_CollgCr	Neighborhood_NAmes	Neighborhood_Sawyer	Condition1_Feedr	Condition1_Norm	Condition2_Norm	...	Foundation_CBlock	Foundation_PConc	BsmtQual_Gd	BsmtQual_TA	BsmtCond_TA	BsmtExposure_No	BsmtFinType1_GLQ	BsmtFinType1_Rec	BsmtFinType1_Unf	BsmtFinType2_Unf	HeatingQC_TA	CentralAir_Y	Electrical_SBrkr	KitchenQual_Fa	KitchenQual_Gd	KitchenQual_TA	Functional_Mod	Functional_Typ	FireplaceQu_Fa	FireplaceQu_NF	FireplaceQu_TA	GarageType_Attchd	GarageType_Detchd	GarageType_NG	GarageFinish_NG	GarageFinish_RFn	GarageFinish_Unf	GarageQual_NG	GarageQual_TA	GarageCond_NG	GarageCond_TA	PavedDrive_Y	PoolQC_NP	Fence_GdWo	Fence_MnPrv	Fence_NF	MiscFeature_NE	MiscFeature_Shed	SaleType_New	SaleType_WD	SaleCondition_Normal	SaleCondition_Partial
0	-0.561119	-0.150083	0.842112	0.534621	-0.570667	-0.301414	-0.28229	0.009986	-0.403595	-0.628924	-0.797828	-0.1294	-1.122539	-1.03986	-0.924643	0.824828	0.333696	0.518931	-0.749421	-0.685547	-0.360803	-0.112837	-0.271032	-0.069193	-0.108754	0.232125	-1.396198	0	0	1	1	1	1	0	1	1	0	0	0	1	0	0	0	1	1	...	0	1	1	0	1	0	1	0	0	1	0	1	1	0	0	1	0	1	0	1	0	0	1	0	0	0	1	0	1	0	1	1	1	0	0	1	1	0	0	1	1	0
1	-0.653665	-0.505027	-0.986602	-0.914670	-0.570667	-0.932224	-0.28229	0.361595	-0.714639	0.353300	-0.797828	-0.1294	-0.394941	0.14623	-0.319271	0.046080	-2.330655	-2.190237	1.284957	-0.685547	-0.360803	-0.112837	-0.271032	-0.069193	1.343344	-0.506915	1.618941	0	1	0	1	1	1	0	1	0	0	1	1	0	0	0	0	1	1	...	1	0	0	1	1	1	0	0	1	1	0	1	1	1	0	0	1	0	0	1	0	0	0	1	1	0	0	1	0	1	0	1	1	1	0	0	0	1	0	1	1	0
2	0.040432	-0.183080	-0.104901	-0.866360	-0.570667	0.850133	-0.28229	-1.121474	-0.311597	-0.633935	-0.797828	-0.1294	-1.126251	0.14623	-0.924643	-0.158854	-0.998480	-0.609889	-0.749421	-0.685547	-0.360803	-0.112837	-0.271032	-0.069193	-0.108754	-0.506915	1.618941	1	0	0	1	1	1	1	1	0	1	0	0	0	0	1	1	0	1	...	1	0	0	1	1	1	0	0	0	1	1	1	1	0	0	1	0	1	1	0	0	0	1	0	0	0	1	0	1	0	1	1	1	0	1	0	1	0	0	1	1	0
3	0.688257	-0.169171	-0.170212	-0.962979	-0.009994	0.428887	-0.28229	-0.276260	0.073922	0.398402	-0.797828	-0.1294	-0.361531	0.14623	-0.319271	-0.527734	0.333696	-0.120733	-0.749421	1.434220	-0.360803	-0.112837	-0.271032	-0.069193	-0.108754	2.079725	-1.396198	1	0	0	1	1	1	1	1	0	0	1	0	0	1	0	0	1	1	...	1	0	0	1	1	1	0	1	0	1	0	1	1	0	0	1	0	1	0	0	1	1	0	0	0	1	0	0	1	0	1	1	1	1	0	0	1	0	0	1	0	0
4	-0.237207	-0.361669	1.201324	1.066028	0.028864	0.473340	-0.28229	-0.053123	0.336776	0.107744	-0.797828	-0.1294	-0.576840	-1.03986	-0.319271	1.193708	0.333696	0.763509	0.062783	0.143927	-0.360803	-0.112837	-0.271032	-0.069193	-0.108754	0.971165	-0.642413	1	0	0	1	1	1	1	1	0	0	1	0	1	0	0	0	1	1	...	0	1	1	0	1	1	1	0	0	1	0	1	1	0	1	0	0	1	0	1	0	1	0	0	0	1	0	0	1	0	1	1	1	0	0	1	1	0	1	0	0	1

	LotFrontage	LotArea	YearBuilt	YearRemodAdd	MasVnrArea	BsmtFinSF1	BsmtFinSF2	BsmtUnfSF	TotalBsmtSF	1stFlrSF	2ndFlrSF	LowQualFinSF	GrLivArea	BedroomAbvGr	TotRmsAbvGrd	GarageYrBlt	GarageCars	GarageArea	WoodDeckSF	OpenPorchSF	EnclosedPorch	3SsnPorch	ScreenPorch	PoolArea	MiscVal	MoSold	YrSold	MSSubClass_160	MSSubClass_50	MSSubClass_60	MSSubClass_70	MSZoning_RL	MSZoning_RM	Street_Pave	Alley_NAA	LotShape_Reg	LandContour_Lvl	LotConfig_CulDSac	LotConfig_Inside	Neighborhood_CollgCr	Neighborhood_Edwards	Neighborhood_Gilbert	Neighborhood_NAmes	Neighborhood_OldTown	Condition1_Norm	Condition2_Norm	...	Foundation_CBlock	Foundation_PConc	BsmtQual_Gd	BsmtQual_TA	BsmtCond_TA	BsmtExposure_No	BsmtFinType1_BLQ	BsmtFinType1_GLQ	BsmtFinType1_Unf	BsmtFinType2_Unf	Heating_GasW	HeatingQC_Fa	HeatingQC_TA	CentralAir_Y	Electrical_SBrkr	KitchenQual_Gd	KitchenQual_TA	Functional_Min2	Functional_Typ	FireplaceQu_Gd	FireplaceQu_NF	FireplaceQu_TA	GarageType_Attchd	GarageType_BuiltIn	GarageType_Detchd	GarageFinish_RFn	GarageFinish_Unf	GarageQual_Fa	GarageQual_TA	GarageCond_Fa	GarageCond_TA	PavedDrive_Y	PoolQC_NP	Fence_NF	MiscFeature_NE	SaleType_WD	SaleCondition_Normal
0	-0.052114	-0.083176	1.005390	0.776169	0.428552	-0.932224	-0.28229	0.713204	-0.372929	-0.704094	1.847518	-0.1294	0.958169	0.146230	0.891472	0.947788	0.333696	-0.148954	0.364459	0.051763	-0.360803	-0.112837	-0.271032	-0.069193	-0.108754	-0.506915	1.618941	0	0	1	0	1	0	1	1	0	1	1	0	0	0	1	0	0	1	1	...	0	1	1	0	1	1	0	0	1	1	0	0	0	1	1	1	0	0	1	1	0	0	0	1	0	0	0	0	1	0	1	1	1	1	1	1	1
1	-0.422299	-0.073531	-2.292826	-1.687625	-0.570667	-0.932224	-0.28229	1.188778	0.089255	-0.175397	0.762719	-0.1294	0.473722	1.332321	0.891472	-2.372137	1.665871	1.473725	-0.749421	-0.685547	1.088712	-0.112837	-0.271032	-0.069193	-0.108754	-0.506915	-1.396198	0	0	0	1	0	1	1	0	1	1	0	1	0	0	0	0	1	1	1	...	0	0	0	1	0	1	0	0	1	1	1	1	0	0	1	0	1	1	0	0	1	0	0	0	0	0	1	1	0	1	0	0	1	1	1	1	1
2	-1.717949	-0.706764	1.103357	0.921098	0.078825	-0.932224	-0.28229	0.920563	-0.171408	-0.473572	0.884529	-0.1294	0.351219	0.146230	0.286100	1.070748	0.333696	-0.402938	-0.749421	-0.071122	-0.360803	-0.112837	-0.271032	-0.069193	-0.108754	-0.876435	-1.396198	1	0	0	0	0	1	1	1	1	1	0	1	0	1	0	0	0	1	1	...	0	1	1	0	1	1	0	0	1	1	0	0	0	1	1	1	0	0	0	0	1	0	0	0	1	0	1	0	1	0	1	1	1	1	1	1	1
3	0.271798	-0.058301	0.907423	0.631240	-0.570667	1.131669	-0.28229	-0.979478	0.117731	-0.142823	1.475193	-0.1294	1.073249	0.146230	0.891472	0.824828	0.333696	0.551855	1.207604	1.925761	-0.360803	-0.112837	-0.271032	-0.069193	-0.108754	-0.137395	-1.396198	0	0	1	0	1	0	1	1	1	1	0	0	1	0	0	0	0	1	1	...	0	1	1	0	1	0	0	1	0	1	0	0	0	1	1	1	0	0	1	0	0	1	0	0	1	1	0	0	1	0	1	1	1	1	1	1	1
4	-0.422299	0.061502	-0.823324	-1.687625	-0.570667	0.295527	-0.28229	-0.528697	-0.309406	-0.293164	0.374306	-0.1294	0.072801	0.146230	-0.319271	-0.650694	-0.998480	-0.835653	-0.749421	-0.685547	-0.360803	-0.112837	-0.271032	-0.069193	-0.108754	-0.876435	0.111371	0	1	0	0	1	0	1	0	1	0	0	1	0	0	0	1	0	0	1	...	1	0	0	1	1	1	1	0	0	1	0	0	1	1	0	0	1	0	1	0	0	1	1	0	0	0	1	0	1	0	1	1	1	1	1	1	1

	LotFrontage	LotArea	YearBuilt	YearRemodAdd	MasVnrArea	BsmtFinSF1	BsmtFinSF2	BsmtUnfSF	TotalBsmtSF	1stFlrSF	2ndFlrSF	LowQualFinSF	GrLivArea	BedroomAbvGr	TotRmsAbvGrd	GarageYrBlt	GarageCars	GarageArea	WoodDeckSF	OpenPorchSF	EnclosedPorch	3SsnPorch	ScreenPorch	PoolArea	MiscVal	MoSold	YrSold
0	0.503164	0.131760	-0.300834	-1.156218	-0.570667	0.058444	0.595815	-0.670693	-0.377310	-0.674026	-0.797828	-0.1294	-1.155949	-1.03986	-0.924643	-0.691681	-0.998480	1.243258	0.333518	-0.685547	-0.360803	-0.112837	1.843566	-0.069193	-0.108754	-0.137395	1.618941
1	0.549437	0.400303	-0.398801	-1.301147	0.028864	1.021594	-0.282290	-0.364162	0.601820	0.410930	-0.797828	-0.1294	-0.352250	0.14623	-0.319271	-0.814641	-0.998480	-0.722771	2.290543	-0.132564	-0.360803	-0.112837	-0.271032	-0.069193	40.227307	-0.137395	1.618941
2	0.225525	0.355935	0.874768	0.631240	-0.570667	0.742175	-0.282290	-0.970462	-0.276549	-0.593844	0.813282	-0.1294	0.204585	0.14623	-0.319271	0.783841	0.333696	0.076810	0.890458	-0.163286	-0.360803	-0.112837	-0.271032	-0.069193	-0.108754	-1.245955	1.618941
3	0.410618	-0.035153	0.907423	0.631240	-0.459643	0.342097	-0.282290	-0.548982	-0.280930	-0.598856	0.760421	-0.1294	0.158182	0.14623	0.286100	0.824828	0.333696	0.020369	2.035278	-0.132564	-0.360803	-0.112837	-0.271032	-0.069193	-0.108754	-0.137395	1.618941
4	-1.208944	-0.540054	0.711490	0.341382	-0.570667	-0.375502	-0.282290	1.012973	0.494488	0.288152	-0.797828	-0.1294	-0.443200	-1.03986	-0.924643	0.578907	0.333696	0.189692	-0.749421	0.574025	-0.360803	-0.112837	2.266486	-0.069193	-0.108754	-1.984995	1.618941

	MSSubClass_20	MSSubClass_30	MSSubClass_85	MSZoning_RL	Street_Pave	Alley_NAA	LotShape_Reg	LandContour_Lvl	LotConfig_CulDSac	LotConfig_FR2	LotConfig_Inside	Neighborhood_BrkSide	Neighborhood_CollgCr	Neighborhood_NAmes	Neighborhood_Sawyer	Condition1_Feedr	Condition1_Norm	Condition2_Norm	HouseStyle_1Story	HouseStyle_SFoyer	OverallQual_3	OverallQual_5	OverallQual_6	OverallQual_7	OverallCond_2	OverallCond_5	...	Foundation_CBlock	Foundation_PConc	BsmtQual_Gd	BsmtQual_TA	BsmtCond_TA	BsmtExposure_No	BsmtFinType1_GLQ	BsmtFinType1_Rec	BsmtFinType1_Unf	BsmtFinType2_Unf	HeatingQC_TA	CentralAir_Y	Electrical_SBrkr	KitchenQual_Fa	KitchenQual_Gd	KitchenQual_TA	Functional_Mod	Functional_Typ	FireplaceQu_Fa	FireplaceQu_NF	FireplaceQu_TA	GarageType_Attchd	GarageType_Detchd	GarageType_NG	GarageFinish_NG	GarageFinish_RFn	GarageFinish_Unf	GarageQual_NG	GarageQual_TA	GarageCond_NG	GarageCond_TA	PavedDrive_Y	PoolQC_NP	Fence_GdWo	Fence_MnPrv	Fence_NF	MiscFeature_NE	MiscFeature_Shed	SaleType_New	SaleType_WD	SaleCondition_Normal	SaleCondition_Partial
0	0	0	1	1	1	1	0	1	1	0	0	0	1	0	0	0	1	1	0	1	0	1	0	0	0	1	...	0	1	1	0	1	0	1	0	0	1	0	1	1	0	0	1	0	1	0	1	0	0	1	0	0	0	1	0	1	0	1	1	1	0	0	1	1	0	0	1	1	0
1	0	1	0	1	1	1	0	1	0	0	1	1	0	0	0	0	1	1	1	0	1	0	0	0	1	0	...	1	0	0	1	1	1	0	0	1	1	0	1	1	1	0	0	1	0	0	1	0	0	0	1	1	0	0	1	0	1	0	1	1	1	0	0	0	1	0	1	1	0
2	1	0	0	1	1	1	1	1	0	1	0	0	0	0	1	1	0	1	1	0	0	1	0	0	0	1	...	1	0	0	1	1	1	0	0	0	1	1	1	1	0	0	1	0	1	1	0	0	0	1	0	0	0	1	0	1	0	1	1	1	0	1	0	1	0	0	1	1	0
3	1	0	0	1	1	1	1	1	0	0	1	0	0	1	0	0	1	1	1	0	0	0	1	0	0	1	...	1	0	0	1	1	1	0	1	0	1	0	1	1	0	0	1	0	1	0	0	1	1	0	0	0	1	0	0	1	0	1	1	1	1	0	0	1	0	0	1	0	0
4	1	0	0	1	1	1	1	1	0	0	1	0	1	0	0	0	1	1	1	0	0	0	0	1	0	1	...	0	1	1	0	1	1	1	0	0	1	0	1	1	0	1	0	0	1	0	1	0	1	0	0	0	1	0	0	1	0	1	1	1	0	0	1	1	0	1	0	0	1

	MSSubClass_160	MSSubClass_50	MSSubClass_60	MSSubClass_70	MSZoning_RL	MSZoning_RM	Street_Pave	Alley_NAA	LotShape_Reg	LandContour_Lvl	LotConfig_CulDSac	LotConfig_Inside	Neighborhood_CollgCr	Neighborhood_Edwards	Neighborhood_Gilbert	Neighborhood_NAmes	Neighborhood_OldTown	Condition1_Norm	Condition2_Norm	BldgType_TwnhsE	HouseStyle_2Story	OverallQual_4	OverallQual_5	OverallQual_7	OverallCond_5	OverallCond_6	RoofStyle_Gable	...	ExterQual_Gd	ExterQual_TA	ExterCond_TA	Foundation_CBlock	Foundation_PConc	BsmtQual_Gd	BsmtQual_TA	BsmtCond_TA	BsmtExposure_No	BsmtFinType1_BLQ	BsmtFinType1_GLQ	BsmtFinType1_Unf	BsmtFinType2_Unf	Heating_GasA	Heating_GasW	HeatingQC_Fa	HeatingQC_TA	CentralAir_Y	Electrical_SBrkr	KitchenQual_Gd	KitchenQual_TA	Functional_Min2	Functional_Typ	FireplaceQu_Gd	FireplaceQu_NF	FireplaceQu_TA	GarageType_Attchd	GarageType_BuiltIn	GarageType_Detchd	GarageFinish_RFn	GarageFinish_Unf	GarageQual_Fa	GarageQual_TA	GarageCond_Fa	GarageCond_TA	PavedDrive_Y	PoolQC_NP	Fence_NF	MiscFeature_NE	SaleType_WD	SaleCondition_Normal
0	0	0	1	0	1	0	1	1	0	1	1	0	0	0	1	0	0	1	1	0	1	0	0	1	1	0	1	...	1	0	1	0	1	1	0	1	1	0	0	1	1	1	0	0	0	1	1	1	0	0	1	1	0	0	0	1	0	0	0	0	1	0	1	1	1	1	1	1	1
1	0	0	0	1	0	1	1	0	1	1	0	1	0	0	0	0	1	1	1	0	1	1	0	0	0	0	1	...	0	1	1	0	0	0	1	0	1	0	0	1	1	0	1	1	0	0	1	0	1	1	0	0	1	0	0	0	0	0	1	1	0	1	0	0	1	1	1	1	1
2	1	0	0	0	0	1	1	1	1	1	0	1	0	1	0	0	0	1	1	1	1	0	0	1	1	0	1	...	1	0	1	0	1	1	0	1	1	0	0	1	1	1	0	0	0	1	1	1	0	0	0	0	1	0	0	0	1	0	1	0	1	0	1	1	1	1	1	1	1
3	0	0	1	0	1	0	1	1	1	1	0	0	1	0	0	0	0	1	1	0	1	0	0	1	0	1	1	...	0	1	1	0	1	1	0	1	0	0	1	0	1	1	0	0	0	1	1	1	0	0	1	0	0	1	0	0	1	1	0	0	1	0	1	1	1	1	1	1	1
4	0	1	0	0	1	0	1	0	1	0	0	1	0	0	0	1	0	0	1	0	0	0	1	0	0	1	1	...	0	1	1	1	0	0	1	1	1	1	0	0	1	1	0	0	1	1	0	0	1	0	1	0	0	1	1	0	0	0	1	0	1	0	1	1	1	1	1	1	1

	MSSubClass_20	MSSubClass_60	MSZoning_RH	MSZoning_RL	Street_Pave	Alley_NAA	LotShape_Reg	LandContour_HLS	LandContour_Lvl	LotConfig_Inside	Neighborhood_Gilbert	Neighborhood_NAmes	Neighborhood_StoneBr	Condition1_Feedr	Condition1_Norm	Condition2_Norm	BldgType_TwnhsE	HouseStyle_1Story	HouseStyle_2Story	OverallQual_5	OverallQual_6	OverallQual_8	OverallCond_5	OverallCond_6	RoofStyle_Gable	...	ExterCond_TA	Foundation_CBlock	Foundation_PConc	BsmtQual_Gd	BsmtQual_TA	BsmtCond_TA	BsmtExposure_No	BsmtFinType1_GLQ	BsmtFinType1_Rec	BsmtFinType2_LwQ	BsmtFinType2_Unf	HeatingQC_Gd	HeatingQC_TA	CentralAir_Y	Electrical_SBrkr	KitchenQual_Gd	KitchenQual_TA	Functional_Typ	FireplaceQu_Gd	FireplaceQu_NF	FireplaceQu_TA	GarageType_Attchd	GarageFinish_RFn	GarageFinish_Unf	GarageQual_TA	GarageCond_TA	PavedDrive_Y	PoolQC_NP	Fence_MnPrv	Fence_NF	MiscFeature_NE	SaleType_WD	SaleCondition_Normal
0	1	0	1	0	1	1	1	0	1	1	0	1	0	1	0	1	0	1	0	1	0	0	0	1	1	...	1	1	0	0	1	1	1	0	1	1	0	0	1	1	1	0	1	1	0	1	0	1	0	1	1	1	1	1	1	0	1	1	1
1	1	0	0	1	1	1	0	0	1	0	0	1	0	0	1	1	0	1	0	0	1	0	0	1	0	...	1	1	0	0	1	1	1	0	0	0	1	0	1	1	1	1	0	1	0	1	0	1	0	1	1	1	1	1	0	1	0	1	1
2	0	1	0	1	1	1	0	0	1	1	1	0	0	0	1	1	0	0	1	1	0	0	1	0	1	...	1	0	1	1	0	1	1	1	0	0	1	1	0	1	1	0	1	1	0	0	1	1	0	0	1	1	1	1	1	0	1	1	1
3	0	1	0	1	1	1	0	0	1	1	1	0	0	0	1	1	0	0	1	0	1	0	0	1	1	...	1	0	1	0	1	1	1	1	0	0	1	0	0	1	1	1	0	1	1	0	0	1	0	0	1	1	1	1	0	1	1	1	1
4	0	0	0	1	1	1	0	1	0	1	0	0	1	0	1	1	1	1	0	0	0	1	1	0	1	...	1	0	1	1	0	1	1	0	0	0	1	0	0	1	1	1	0	1	0	1	0	1	1	0	1	1	1	1	0	1	1	1	1

	principal component 1	principal component 2
0	-0.895413	-2.186318
1	-2.665399	0.434821
2	-2.098681	-0.797977
3	-0.056531	-0.031794
4	0.845449	-2.374163

	Vif	Variables
0	3.427303	LotFrontage
1	3.999449	LotArea
2	19.790053	YearBuilt
3	4.192063	YearRemodAdd
4	3.712573	MasVnrArea
5	inf	BsmtFinSF1
6	inf	BsmtFinSF2
7	inf	BsmtUnfSF
8	inf	TotalBsmtSF
9	inf	1stFlrSF
10	inf	2ndFlrSF
11	inf	LowQualFinSF
12	inf	GrLivArea
13	4.432669	BedroomAbvGr
14	7.904046	TotRmsAbvGrd
15	6.608873	GarageYrBlt
16	9.654663	GarageCars
17	10.397869	GarageArea
18	1.720052	WoodDeckSF
19	2.017527	OpenPorchSF
20	2.086494	EnclosedPorch
21	1.291584	3SsnPorch
22	1.537126	ScreenPorch
23	4011.826053	PoolArea
24	15.981804	MiscVal
25	1.423591	MoSold
26	1.437997	YrSold
27	12.261775	MSSubClass_160
28	4.485264	MSSubClass_180
29	52.742532	MSSubClass_190
30	148.036504	MSSubClass_20
31	34.876756	MSSubClass_30
32	2.750924	MSSubClass_40
33	25.913360	MSSubClass_45
34	75.497906	MSSubClass_50
35	123.866874	MSSubClass_60
36	37.687851	MSSubClass_70
37	13.421388	MSSubClass_75
38	44.791574	MSSubClass_80
39	13.353473	MSSubClass_85
40	inf	MSSubClass_90
41	17.310744	MSZoning_FV
42	5.422079	MSZoning_RH
43	53.826513	MSZoning_RL
44	36.078449	MSZoning_RM
45	3.215226	Street_Pave
46	3.554404	Alley_NAA
47	3.582523	Alley_Pave
48	1.524013	LotShape_IR2
49	1.880076	LotShape_IR3
50	1.903818	LotShape_Reg
51	2.887861	LandContour_HLS
52	2.980395	LandContour_Low
53	4.024932	LandContour_Lvl
54	1.926402	Utilities_NoSeWa
55	1.950570	LotConfig_CulDSac
56	1.559877	LotConfig_FR2
57	1.567949	LotConfig_FR3
58	2.052214	LotConfig_Inside
59	2.208504	LandSlope_Mod
60	3.973680	LandSlope_Sev
61	1.413362	Neighborhood_Blueste
62	5.419048	Neighborhood_BrDale
63	10.298785	Neighborhood_BrkSide
64	4.386085	Neighborhood_ClearCr
65	14.200442	Neighborhood_CollgCr
66	8.563209	Neighborhood_Crawfor
67	11.539715	Neighborhood_Edwards
68	8.353482	Neighborhood_Gilbert
69	10.310404	Neighborhood_IDOTRR
70	6.711799	Neighborhood_MeadowV
71	5.606135	Neighborhood_Mitchel
72	22.902358	Neighborhood_NAmes
73	4.257706	Neighborhood_NPkVill
74	9.953334	Neighborhood_NWAmes
75	5.938724	Neighborhood_NoRidge
76	7.855056	Neighborhood_NridgHt
77	20.591891	Neighborhood_OldTown
78	5.794227	Neighborhood_SWISU
79	9.321027	Neighborhood_Sawyer
80	8.322533	Neighborhood_SawyerW
81	13.880332	Neighborhood_Somerst
82	3.772372	Neighborhood_StoneBr
83	5.983023	Neighborhood_Timber
84	2.904420	Neighborhood_Veenker
85	4.768181	Condition1_Feedr
86	7.666565	Condition1_Norm
87	1.818198	Condition1_PosA
88	2.455862	Condition1_PosN
89	2.060942	Condition1_RRAe
90	2.922049	Condition1_RRAn
91	1.300616	Condition1_RRNe
92	2.093322	Condition1_RRNn
93	10.237745	Condition2_Feedr
94	22.657792	Condition2_Norm
95	5.059141	Condition2_PosA
96	5.122666	Condition2_PosN
97	inf	Condition2_RRAe
98	2.964216	Condition2_RRAn
99	4.922677	Condition2_RRNn
100	40.937998	BldgType_2fmCon
101	inf	BldgType_Duplex
102	22.616166	BldgType_Twnhs
103	47.396503	BldgType_TwnhsE
104	22.965994	HouseStyle_1.5Unf
105	56.428032	HouseStyle_1Story
106	6.019891	HouseStyle_2.5Fin
107	5.289820	HouseStyle_2.5Unf
108	37.302869	HouseStyle_2Story
109	10.332908	HouseStyle_SFoyer
110	24.742278	HouseStyle_SLvl
111	37.393888	OverallQual_10
112	8.742462	OverallQual_2
113	36.255497	OverallQual_3
114	174.456349	OverallQual_4
115	461.587063	OverallQual_5
116	436.827555	OverallQual_6
117	389.839838	OverallQual_7
118	245.516190	OverallQual_8
119	71.998566	OverallQual_9
120	inf	OverallCond_2
121	inf	OverallCond_3
122	inf	OverallCond_4
123	inf	OverallCond_5
124	inf	OverallCond_6
125	inf	OverallCond_7
126	inf	OverallCond_8
127	inf	OverallCond_9
128	262.399021	RoofStyle_Gable
129	18.323231	RoofStyle_Gambrel
130	240.734519	RoofStyle_Hip
131	11.340809	RoofStyle_Mansard
132	inf	RoofStyle_Shed
133	2700.766719	RoofMatl_CompShg
134	174.363933	RoofMatl_Membran
135	173.802572	RoofMatl_Roll
136	852.637461	RoofMatl_Tar&Grv
137	517.627943	RoofMatl_WdShake
138	854.590541	RoofMatl_WdShngl
139	4.876335	Exterior1st_BrkComm
140	61.397688	Exterior1st_BrkFace
141	inf	Exterior1st_CBlock
142	94.614071	Exterior1st_CemntBd
143	213.882917	Exterior1st_HdBoard
144	231.069581	Exterior1st_MetalSd
145	112.564784	Exterior1st_Plywood
146	4.062381	Exterior1st_Stone
147	34.602848	Exterior1st_Stucco
148	425.218706	Exterior1st_VinylSd
149	210.423161	Exterior1st_Wd Sdng
150	32.938163	Exterior1st_WdShing
151	4.615536	Exterior2nd_AsphShn
152	13.245295	Exterior2nd_Brk Cmn
153	30.295943	Exterior2nd_BrkFace
154	inf	Exterior2nd_CBlock
155	91.359437	Exterior2nd_CmentBd
156	192.666290	Exterior2nd_HdBoard
157	10.701847	Exterior2nd_ImStucc
158	216.099506	Exterior2nd_MetalSd
159	2.861664	Exterior2nd_Other
160	134.825448	Exterior2nd_Plywood
161	4.997426	Exterior2nd_Stone
162	35.572751	Exterior2nd_Stucco
163	399.756520	Exterior2nd_VinylSd
164	194.924924	Exterior2nd_Wd Sdng
165	40.216584	Exterior2nd_Wd Shng
166	29.147382	MasVnrType_BrkFace
167	33.423333	MasVnrType_None
168	11.307175	MasVnrType_Stone
169	1.685112	MasVnrType_nan
170	8.052741	ExterQual_Fa
171	19.876674	ExterQual_Gd
172	25.635366	ExterQual_TA
173	26.442209	ExterCond_Fa
174	125.104408	ExterCond_Gd
175	4.197280	ExterCond_Po
176	148.024295	ExterCond_TA
177	7.677131	Foundation_CBlock
178	9.153874	Foundation_PConc
179	5.887688	Foundation_Slab
180	1.746870	Foundation_Stone
181	1.426486	Foundation_Wood
182	3.673268	BsmtQual_Fa
183	8.423189	BsmtQual_Gd
184	inf	BsmtQual_NB
185	13.395188	BsmtQual_TA
186	3.721399	BsmtCond_Gd
187	inf	BsmtCond_NB
188	inf	BsmtCond_Po
189	5.514594	BsmtCond_TA
190	2.437357	BsmtExposure_Gd
191	2.133235	BsmtExposure_Mn
192	28.763158	BsmtExposure_NB
193	3.543014	BsmtExposure_No
194	2.271547	BsmtFinType1_BLQ
195	3.914006	BsmtFinType1_GLQ
196	2.075776	BsmtFinType1_LwQ
197	inf	BsmtFinType1_NB
198	2.289346	BsmtFinType1_Rec
199	5.219077	BsmtFinType1_Unf
200	3.992223	BsmtFinType2_BLQ
201	2.786058	BsmtFinType2_GLQ
202	5.027166	BsmtFinType2_LwQ
203	inf	BsmtFinType2_NB
204	6.671440	BsmtFinType2_Rec
205	22.516866	BsmtFinType2_Unf
206	2.367540	Heating_GasW
207	6.569004	Heating_Grav
208	1.818154	Heating_OthW
209	2.167037	Heating_Wall
210	2.415494	HeatingQC_Fa
211	1.863640	HeatingQC_Gd
212	1.634062	HeatingQC_Po
213	2.679859	HeatingQC_TA
214	3.092275	CentralAir_Y
215	2.479705	Electrical_FuseF
216	2.340425	Electrical_FuseP
217	inf	Electrical_Mix
218	2.447257	Electrical_SBrkr
219	1.198662	Electrical_nan
220	3.885402	KitchenQual_Fa
221	11.535085	KitchenQual_Gd
222	14.338955	KitchenQual_TA
223	2.692766	Functional_Maj2
224	5.099321	Functional_Min1
225	6.448350	Functional_Min2
226	3.590562	Functional_Mod
227	13.171473	Functional_Typ
228	3.554932	FireplaceQu_Fa
229	18.241309	FireplaceQu_Gd
230	24.980039	FireplaceQu_NF
231	2.401982	FireplaceQu_Po
232	16.986905	FireplaceQu_TA
233	128.433846	GarageType_Attchd
234	8.687245	GarageType_Basment
235	29.559992	GarageType_BuiltIn
236	6.091227	GarageType_CarPort
237	104.829853	GarageType_Detchd
238	inf	GarageType_NG
239	inf	GarageFinish_NG
240	2.444350	GarageFinish_RFn
241	4.572504	GarageFinish_Unf
242	inf	GarageQual_Fa
243	inf	GarageQual_Gd
244	inf	GarageQual_NG
245	inf	GarageQual_Po
246	inf	GarageQual_TA
247	inf	GarageCond_Fa
248	inf	GarageCond_Gd
249	inf	GarageCond_NG
250	inf	GarageCond_Po
251	inf	GarageCond_TA
252	2.102860	PavedDrive_P
253	2.896349	PavedDrive_Y
254	inf	PoolQC_Fa
255	204.949954	PoolQC_Gd
256	3606.247680	PoolQC_NP
257	2.836423	Fence_GdWo
258	4.707714	Fence_MnPrv
259	1.340711	Fence_MnWw
260	6.083139	Fence_NF
261	inf	MiscFeature_NE
262	inf	MiscFeature_Shed
263	inf	MiscFeature_TenC
264	1.490160	SaleType_CWD
265	1.310236	SaleType_Con
266	2.142271	SaleType_ConLD
267	1.500495	SaleType_ConLI
268	2.429533	SaleType_ConLw
269	43.538066	SaleType_New
270	1.414532	SaleType_Oth
271	5.973698	SaleType_WD
272	2.085595	SaleCondition_AdjLand
273	2.235964	SaleCondition_Alloca
274	1.549010	SaleCondition_Family
275	3.882058	SaleCondition_Normal
276	40.600436	SaleCondition_Partial

	MSSubClass_20	MSSubClass_30	MSSubClass_85	MSZoning_RL	Street_Pave	Alley_NAA	LotShape_Reg	LandContour_Lvl	LotConfig_CulDSac	LotConfig_FR2	LotConfig_Inside	Neighborhood_BrkSide	Neighborhood_CollgCr	Neighborhood_NAmes	Neighborhood_Sawyer	Condition1_Feedr	Condition1_Norm	Condition2_Norm	HouseStyle_1Story	HouseStyle_SFoyer	OverallQual_3	OverallQual_5	OverallQual_6	OverallQual_7	OverallCond_2	OverallCond_5	...	Foundation_CBlock	Foundation_PConc	BsmtQual_Gd	BsmtQual_TA	BsmtCond_TA	BsmtExposure_No	BsmtFinType1_GLQ	BsmtFinType1_Rec	BsmtFinType1_Unf	BsmtFinType2_Unf	HeatingQC_TA	CentralAir_Y	Electrical_SBrkr	KitchenQual_Fa	KitchenQual_Gd	KitchenQual_TA	Functional_Mod	Functional_Typ	FireplaceQu_Fa	FireplaceQu_NF	FireplaceQu_TA	GarageType_Attchd	GarageType_Detchd	GarageType_NG	GarageFinish_NG	GarageFinish_RFn	GarageFinish_Unf	GarageQual_NG	GarageQual_TA	GarageCond_NG	GarageCond_TA	PavedDrive_Y	PoolQC_NP	Fence_GdWo	Fence_MnPrv	Fence_NF	MiscFeature_NE	MiscFeature_Shed	SaleType_New	SaleType_WD	SaleCondition_Normal	SaleCondition_Partial
0	0	0	1	1	1	1	0	1	1	0	0	0	1	0	0	0	1	1	0	1	0	1	0	0	0	1	...	0	1	1	0	1	0	1	0	0	1	0	1	1	0	0	1	0	1	0	1	0	0	1	0	0	0	1	0	1	0	1	1	1	0	0	1	1	0	0	1	1	0
1	0	1	0	1	1	1	0	1	0	0	1	1	0	0	0	0	1	1	1	0	1	0	0	0	1	0	...	1	0	0	1	1	1	0	0	1	1	0	1	1	1	0	0	1	0	0	1	0	0	0	1	1	0	0	1	0	1	0	1	1	1	0	0	0	1	0	1	1	0
2	1	0	0	1	1	1	1	1	0	1	0	0	0	0	1	1	0	1	1	0	0	1	0	0	0	1	...	1	0	0	1	1	1	0	0	0	1	1	1	1	0	0	1	0	1	1	0	0	0	1	0	0	0	1	0	1	0	1	1	1	0	1	0	1	0	0	1	1	0
3	1	0	0	1	1	1	1	1	0	0	1	0	0	1	0	0	1	1	1	0	0	0	1	0	0	1	...	1	0	0	1	1	1	0	1	0	1	0	1	1	0	0	1	0	1	0	0	1	1	0	0	0	1	0	0	1	0	1	1	1	1	0	0	1	0	0	1	0	0
4	1	0	0	1	1	1	1	1	0	0	1	0	1	0	0	0	1	1	1	0	0	0	0	1	0	1	...	0	1	1	0	1	1	1	0	0	1	0	1	1	0	1	0	0	1	0	1	0	1	0	0	0	1	0	0	1	0	1	1	1	0	0	1	1	0	1	0	0	1

	MSSubClass_160	MSSubClass_50	MSSubClass_60	MSSubClass_70	MSZoning_RL	MSZoning_RM	Street_Pave	Alley_NAA	LotShape_Reg	LandContour_Lvl	LotConfig_CulDSac	LotConfig_Inside	Neighborhood_CollgCr	Neighborhood_Edwards	Neighborhood_Gilbert	Neighborhood_NAmes	Neighborhood_OldTown	Condition1_Norm	Condition2_Norm	BldgType_TwnhsE	HouseStyle_2Story	OverallQual_4	OverallQual_5	OverallQual_7	OverallCond_5	OverallCond_6	RoofStyle_Gable	...	ExterQual_Gd	ExterQual_TA	ExterCond_TA	Foundation_CBlock	Foundation_PConc	BsmtQual_Gd	BsmtQual_TA	BsmtCond_TA	BsmtExposure_No	BsmtFinType1_BLQ	BsmtFinType1_GLQ	BsmtFinType1_Unf	BsmtFinType2_Unf	Heating_GasA	Heating_GasW	HeatingQC_Fa	HeatingQC_TA	CentralAir_Y	Electrical_SBrkr	KitchenQual_Gd	KitchenQual_TA	Functional_Min2	Functional_Typ	FireplaceQu_Gd	FireplaceQu_NF	FireplaceQu_TA	GarageType_Attchd	GarageType_BuiltIn	GarageType_Detchd	GarageFinish_RFn	GarageFinish_Unf	GarageQual_Fa	GarageQual_TA	GarageCond_Fa	GarageCond_TA	PavedDrive_Y	PoolQC_NP	Fence_NF	MiscFeature_NE	SaleType_WD	SaleCondition_Normal
0	0	0	1	0	1	0	1	1	0	1	1	0	0	0	1	0	0	1	1	0	1	0	0	1	1	0	1	...	1	0	1	0	1	1	0	1	1	0	0	1	1	1	0	0	0	1	1	1	0	0	1	1	0	0	0	1	0	0	0	0	1	0	1	1	1	1	1	1	1
1	0	0	0	1	0	1	1	0	1	1	0	1	0	0	0	0	1	1	1	0	1	1	0	0	0	0	1	...	0	1	1	0	0	0	1	0	1	0	0	1	1	0	1	1	0	0	1	0	1	1	0	0	1	0	0	0	0	0	1	1	0	1	0	0	1	1	1	1	1
2	1	0	0	0	0	1	1	1	1	1	0	1	0	1	0	0	0	1	1	1	1	0	0	1	1	0	1	...	1	0	1	0	1	1	0	1	1	0	0	1	1	1	0	0	0	1	1	1	0	0	0	0	1	0	0	0	1	0	1	0	1	0	1	1	1	1	1	1	1
3	0	0	1	0	1	0	1	1	1	1	0	0	1	0	0	0	0	1	1	0	1	0	0	1	0	1	1	...	0	1	1	0	1	1	0	1	0	0	1	0	1	1	0	0	0	1	1	1	0	0	1	0	0	1	0	0	1	1	0	0	1	0	1	1	1	1	1	1	1
4	0	1	0	0	1	0	1	0	1	0	0	1	0	0	0	1	0	0	1	0	0	0	1	0	0	1	1	...	0	1	1	1	0	0	1	1	1	1	0	0	1	1	0	0	1	1	0	0	1	0	1	0	0	1	1	0	0	0	1	0	1	0	1	1	1	1	1	1	1

	MSSubClass_20	MSSubClass_60	MSZoning_RH	MSZoning_RL	Street_Pave	Alley_NAA	LotShape_Reg	LandContour_HLS	LandContour_Lvl	LotConfig_Inside	Neighborhood_Gilbert	Neighborhood_NAmes	Neighborhood_StoneBr	Condition1_Feedr	Condition1_Norm	Condition2_Norm	BldgType_TwnhsE	HouseStyle_1Story	HouseStyle_2Story	OverallQual_5	OverallQual_6	OverallQual_8	OverallCond_5	OverallCond_6	RoofStyle_Gable	...	ExterCond_TA	Foundation_CBlock	Foundation_PConc	BsmtQual_Gd	BsmtQual_TA	BsmtCond_TA	BsmtExposure_No	BsmtFinType1_GLQ	BsmtFinType1_Rec	BsmtFinType2_LwQ	BsmtFinType2_Unf	HeatingQC_Gd	HeatingQC_TA	CentralAir_Y	Electrical_SBrkr	KitchenQual_Gd	KitchenQual_TA	Functional_Typ	FireplaceQu_Gd	FireplaceQu_NF	FireplaceQu_TA	GarageType_Attchd	GarageFinish_RFn	GarageFinish_Unf	GarageQual_TA	GarageCond_TA	PavedDrive_Y	PoolQC_NP	Fence_MnPrv	Fence_NF	MiscFeature_NE	SaleType_WD	SaleCondition_Normal
0	1	0	1	0	1	1	1	0	1	1	0	1	0	1	0	1	0	1	0	1	0	0	0	1	1	...	1	1	0	0	1	1	1	0	1	1	0	0	1	1	1	0	1	1	0	1	0	1	0	1	1	1	1	1	1	0	1	1	1
1	1	0	0	1	1	1	0	0	1	0	0	1	0	0	1	1	0	1	0	0	1	0	0	1	0	...	1	1	0	0	1	1	1	0	0	0	1	0	1	1	1	1	0	1	0	1	0	1	0	1	1	1	1	1	0	1	0	1	1
2	0	1	0	1	1	1	0	0	1	1	1	0	0	0	1	1	0	0	1	1	0	0	1	0	1	...	1	0	1	1	0	1	1	1	0	0	1	1	0	1	1	0	1	1	0	0	1	1	0	0	1	1	1	1	1	0	1	1	1
3	0	1	0	1	1	1	0	0	1	1	1	0	0	0	1	1	0	0	1	0	1	0	0	1	1	...	1	0	1	0	1	1	1	1	0	0	1	0	0	1	1	1	0	1	1	0	0	1	0	0	1	1	1	1	0	1	1	1	1
4	0	0	0	1	1	1	0	1	0	1	0	0	1	0	1	1	1	1	0	0	0	1	1	0	1	...	1	0	1	1	0	1	1	0	0	0	1	0	0	1	1	1	0	1	0	1	0	1	1	0	1	1	1	1	0	1	1	1	1